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ORCIDPieter Abbeel
Person information
- affiliation: University of California, Berkeley, USA
- affiliation: Stanford University, USA
- award (2021): ACM Prize in Computing
- award (2013): Presidential Early Career Award for Scientists and Engineers
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Books and Theses
- 2008
[b1]Pieter Abbeel:
Apprenticeship learning and reinforcement learning with application to robotic control. Stanford University, USA, 2008
Journal Articles
- 2023
- [j27]Kourosh Hakhamaneshi
, Marcel Nassar, Mariano Phielipp, Pieter Abbeel, Vladimir Stojanovic:
Pretraining Graph Neural Networks for Few-Shot Analog Circuit Modeling and Design. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 42(7): 2163-2173 (2023) - 2022
- [j26]Freek Stulp, Michael Spranger, Kim Listmann, Stéphane Doncieux, Moritz Tenorth, George Konidaris, Pieter Abbeel:
Innovation Paths for Machine Learning in Robotics [Industry Activities]. IEEE Robotics Autom. Mag. 29(4): 141-144 (2022) - 2021
- [j25]Gregory Kahn, Pieter Abbeel, Sergey Levine:
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. IEEE Robotics Autom. Lett. 6(2): 1312-1319 (2021) - [j24]Gregory Kahn, Pieter Abbeel, Sergey Levine:
LaND: Learning to Navigate From Disengagements. IEEE Robotics Autom. Lett. 6(2): 1872-1879 (2021) - [j23]Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, Angjoo Kanazawa:
AMP: adversarial motion priors for stylized physics-based character control. ACM Trans. Graph. 40(4): 144:1-144:20 (2021) - 2019
- [j22]Sandy H. Huang, David Held, Pieter Abbeel, Anca D. Dragan:
Enabling robots to communicate their objectives. Auton. Robots 43(2): 309-326 (2019) - [j21]Zongheng Yang, Eric Liang, Amog Kamsetty, Chenggang Wu, Yan Duan, Xi Chen, Pieter Abbeel, Joseph M. Hellerstein, Sanjay Krishnan, Ion Stoica:
Deep Unsupervised Cardinality Estimation. Proc. VLDB Endow. 13(3): 279-292 (2019) - 2018
- [j20]Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, Jan Peters:
An Algorithmic Perspective on Imitation Learning. Found. Trends Robotics 7(1-2): 1-179 (2018) - [j19]Niko Sünderhauf, Oliver Brock, Walter J. Scheirer, Raia Hadsell, Dieter Fox, Jürgen Leitner, Ben Upcroft, Pieter Abbeel, Wolfram Burgard, Michael Milford, Peter Corke:
The limits and potentials of deep learning for robotics. Int. J. Robotics Res. 37(4-5): 405-420 (2018) - [j18]Xue Bin Peng, Pieter Abbeel, Sergey Levine, Michiel van de Panne:
DeepMimic: example-guided deep reinforcement learning of physics-based character skills. ACM Trans. Graph. 37(4): 143 (2018) - [j17]Xue Bin Peng, Angjoo Kanazawa, Jitendra Malik, Pieter Abbeel, Sergey Levine:
SFV: reinforcement learning of physical skills from videos. ACM Trans. Graph. 37(6): 178 (2018) - 2017
- [j16]Berk Çalli, Arjun Singh, James Bruce, Aaron Walsman, Kurt Konolige, Siddhartha S. Srinivasa, Pieter Abbeel, Aaron M. Dollar:
Yale-CMU-Berkeley dataset for robotic manipulation research. Int. J. Robotics Res. 36(3): 261-268 (2017) - [j15]Sanjay Krishnan, Animesh Garg, Sachin Patil, Colin Lea, Gregory D. Hager, Pieter Abbeel, Ken Goldberg:
Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning. Int. J. Robotics Res. 36(13-14): 1595-1618 (2017) - 2016
- [j14]Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel:
End-to-End Training of Deep Visuomotor Policies. J. Mach. Learn. Res. 17: 39:1-39:40 (2016) - 2015
- [j13]Berk Çalli, Aaron Walsman, Arjun Singh, Siddhartha S. Srinivasa, Pieter Abbeel, Aaron M. Dollar:
Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set. IEEE Robotics Autom. Mag. 22(3): 36-52 (2015) - [j12]Ben Kehoe, Sachin Patil, Pieter Abbeel, Ken Goldberg:
A Survey of Research on Cloud Robotics and Automation. IEEE Trans Autom. Sci. Eng. 12(2): 398-409 (2015) - [j11]Sachin Patil, Jia Pan, Pieter Abbeel, Ken Goldberg:
Planning Curvature and Torsion Constrained Ribbons in 3D With Application to Intracavitary Brachytherapy. IEEE Trans Autom. Sci. Eng. 12(4): 1332-1345 (2015) - 2014
- [j10]John Schulman, Yan Duan, Jonathan Ho, Alex X. Lee, Ibrahim Awwal, Henry Bradlow, Jia Pan, Sachin Patil, Ken Goldberg, Pieter Abbeel:
Motion planning with sequential convex optimization and convex collision checking. Int. J. Robotics Res. 33(9): 1251-1270 (2014) - [j9]Timothy Hunter, Pieter Abbeel, Alexandre M. Bayen:
The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data. IEEE Trans. Intell. Transp. Syst. 15(2): 507-529 (2014) - 2013
- [j8]Timothy Hunter, Tathagata Das, Matei Zaharia, Pieter Abbeel, Alexandre M. Bayen:
Large-Scale Estimation in Cyberphysical Systems Using Streaming Data: A Case Study With Arterial Traffic Estimation. IEEE Trans Autom. Sci. Eng. 10(4): 884-898 (2013) - 2012
- [j7]Stephen Miller, Jur van den Berg, Mario Fritz, Trevor Darrell, Kenneth Y. Goldberg, Pieter Abbeel:
A geometric approach to robotic laundry folding. Int. J. Robotics Res. 31(2): 249-267 (2012) - [j6]Aude Hofleitner, Ryan Herring, Pieter Abbeel, Alexandre M. Bayen:
Learning the Dynamics of Arterial Traffic From Probe Data Using a Dynamic Bayesian Network. IEEE Trans. Intell. Transp. Syst. 13(4): 1679-1693 (2012) - 2011
- [j5]Jur van den Berg, Pieter Abbeel, Kenneth Y. Goldberg:
LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information. Int. J. Robotics Res. 30(7): 895-913 (2011) - 2010
- [j4]Pieter Abbeel, Adam Coates, Andrew Y. Ng:
Autonomous Helicopter Aerobatics through Apprenticeship Learning. Int. J. Robotics Res. 29(13): 1608-1639 (2010) - 2009
- [j3]Adam Coates, Pieter Abbeel, Andrew Y. Ng:
Apprenticeship learning for helicopter control. Commun. ACM 52(7): 97-105 (2009) - 2008
- [j2]Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller:
Max-margin Classification of Data with Absent Features. J. Mach. Learn. Res. 9: 1-21 (2008) - 2006
- [j1]Pieter Abbeel, Daphne Koller, Andrew Y. Ng:
Learning Factor Graphs in Polynomial Time and Sample Complexity. J. Mach. Learn. Res. 7: 1743-1788 (2006)
Conference and Workshop Papers
- 2024
- [c340]Kuba Grudzien Kuba, Masatoshi Uehara, Sergey Levine, Pieter Abbeel:
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization. AISTATS 2024: 2449-2457 - [c339]Hao Liu, Carmelo Sferrazza, Pieter Abbeel:
Chain of Hindsight aligns Language Models with Feedback. ICLR 2024 - [c338]Hao Liu, Matei Zaharia, Pieter Abbeel:
RingAttention with Blockwise Transformers for Near-Infinite Context. ICLR 2024 - [c337]Yilun Du, Sherry Yang, Pete Florence, Fei Xia, Ayzaan Wahid, Brian Ichter, Pierre Sermanet, Tianhe Yu, Pieter Abbeel, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Andy Zeng, Jonathan Tompson:
Video Language Planning. ICLR 2024 - [c336]Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song:
The False Promise of Imitating Proprietary Language Models. ICLR 2024 - [c335]Vint Lee, Pieter Abbeel, Youngwoon Lee:
DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing. ICLR 2024 - [c334]Sam Toyer, Olivia Watkins, Ethan Adrian Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, Alan Ritter, Stuart Russell:
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game. ICLR 2024 - [c333]Sherry Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans, Igor Mordatch, Ekin Dogus Cubuk:
Scalable Diffusion for Materials Generation. ICLR 2024 - [c332]Sherry Yang, Yilun Du, Bo Dai, Dale Schuurmans, Joshua B. Tenenbaum, Pieter Abbeel:
Probabilistic Adaptation of Black-Box Text-to-Video Models. ICLR 2024 - [c331]Sherry Yang, Yilun Du, Seyed Kamyar Seyed Ghasemipour, Jonathan Tompson, Leslie Pack Kaelbling, Dale Schuurmans, Pieter Abbeel:
Learning Interactive Real-World Simulators. ICLR 2024 - [c330]Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine:
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings. ICML 2024 - [c329]Huiwon Jang, Dongyoung Kim, Junsu Kim, Jinwoo Shin, Pieter Abbeel, Younggyo Seo:
Visual Representation Learning with Stochastic Frame Prediction. ICML 2024 - [c328]Jessy Lin, Yuqing Du, Olivia Watkins, Danijar Hafner, Pieter Abbeel, Dan Klein, Anca D. Dragan:
Learning to Model the World With Language. ICML 2024 - [c327]Michael Psenka, Alejandro Escontrela, Pieter Abbeel, Yi Ma:
Learning a Diffusion Model Policy from Rewards via Q-Score Matching. ICML 2024 - [c326]Sherry Yang, Jacob C. Walker, Jack Parker-Holder, Yilun Du, Jake Bruce, André Barreto, Pieter Abbeel, Dale Schuurmans:
Position: Video as the New Language for Real-World Decision Making. ICML 2024 - [c325]Xingyu Lin, John So, Sashwat Mahalingam, Fangchen Liu, Pieter Abbeel:
SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Network. ICRA 2024: 4781-4787 - [c324]Abby O'Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alexander Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Beomjoon Kim, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu, Charlotte Le, Chelsea Finn, Chen Wang, Chenfeng Xu, Cheng Chi, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Driess, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Ge Yan, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su, Haoshu Fang, Haochen Shi, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim, Jaimyn Drake, Jan Peters, Jan Schneider, Jasmine Hsu, Jeannette Bohg, Jeffrey Bingham, Jeffrey Wu, Jensen Gao, Jiaheng Hu, Jiajun Wu, Jialin Wu, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jingyun Yang, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kaiyuan Wang, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Ken Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Lin, Kevin Zhang, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Lawrence Yunliang Chen, Lerrel Pinto, Li Fei-Fei, Liam Tan, Linxi Jim Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Ning Liu, Norman Di Palo, Nur Muhammad (Mahi) Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Quan Vuong, Rafael Rafailov, Ran Tian, Ria Doshi, Roberto Martín-Martín, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Ruohan Zhang, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante, Sean Kirmani, Sergey Levine, Shan Lin, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wei Zhan, Wenxuan Zhou, Wolfram Burgard, Xi Chen, Xiaolong Wang, Xinghao Zhu, Xinyang Geng, Xiyuan Liu, Liangwei Xu, Xuanlin Li, Yao Lu, Yecheng Jason Ma, Yejin Kim, Yevgen Chebotar, Yifan Zhou, Yifeng Zhu, Yilin Wu, Ying Xu, Yixuan Wang, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yue Cao, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zhuo Xu, Zichen Jeff Cui, Zichen Zhang, Zipeng Lin:
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration. ICRA 2024: 6892-6903 - [c323]Nikhil Mishra, Maximilian Sieb, Pieter Abbeel, Xi Chen:
Closing the Visual Sim-to-Real Gap with Object-Composable NeRFs. ICRA 2024: 11202-11208 - 2023
- [c322]Joey Hejna, Pieter Abbeel, Lerrel Pinto:
Improving Long-Horizon Imitation through Instruction Prediction. AAAI 2023: 7857-7865 - [c321]Kevin Zakka, Philipp Wu, Laura M. Smith, Nimrod Gileadi, Taylor Howell, Xue Bin Peng, Sumeet Singh, Yuval Tassa, Pete Florence, Andy Zeng, Pieter Abbeel:
RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning. CoRL 2023: 2975-2994 - [c320]Amber Xie, Youngwoon Lee, Pieter Abbeel, Stephen James:
Language-Conditioned Path Planning. CoRL 2023: 3384-3396 - [c319]Ajay Jain, Amber Xie, Pieter Abbeel:
VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models. CVPR 2023: 1911-1920 - [c318]Changyeon Kim, Jongjin Park, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee:
Preference Transformer: Modeling Human Preferences using Transformers for RL. ICLR 2023 - [c317]Sherry Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Dichotomy of Control: Separating What You Can Control from What You Cannot. ICLR 2023 - [c316]Weirui Ye, Yunsheng Zhang, Pieter Abbeel, Yang Gao:
Become a Proficient Player with Limited Data through Watching Pure Videos. ICLR 2023 - [c315]Abdus Salam Azad, Izzeddin Gur, Jasper Emhoff, Nathaniel Alexis, Aleksandra Faust, Pieter Abbeel, Ion Stoica:
CLUTR: Curriculum Learning via Unsupervised Task Representation Learning. ICML 2023: 1361-1395 - [c314]Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, Jacob Andreas:
Guiding Pretraining in Reinforcement Learning with Large Language Models. ICML 2023: 8657-8677 - [c313]Hao Liu, Pieter Abbeel:
Emergent Agentic Transformer from Chain of Hindsight Experience. ICML 2023: 21362-21374 - [c312]Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel:
Controllability-Aware Unsupervised Skill Discovery. ICML 2023: 27225-27245 - [c311]Younggyo Seo, Junsu Kim, Stephen James, Kimin Lee, Jinwoo Shin, Pieter Abbeel:
Multi-View Masked World Models for Visual Robotic Manipulation. ICML 2023: 30613-30632 - [c310]David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, Ofir Nachum:
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets. ICML 2023: 35024-35036 - [c309]Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran:
Masked Trajectory Models for Prediction, Representation, and Control. ICML 2023: 37607-37623 - [c308]Wilson Yan, Danijar Hafner, Stephen James, Pieter Abbeel:
Temporally Consistent Transformers for Video Generation. ICML 2023: 39062-39098 - [c307]Tianjun Zhang, Fangchen Liu, Justin Wong, Pieter Abbeel, Joseph E. Gonzalez:
The Wisdom of Hindsight Makes Language Models Better Instruction Followers. ICML 2023: 41414-41428 - [c306]Kai Chen, Stephen James, Congying Sui, Yun-Hui Liu, Pieter Abbeel, Qi Dou:
StereoPose: Category-Level 6D Transparent Object Pose Estimation from Stereo Images via Back-View NOCS. ICRA 2023: 2855-2861 - [c305]Yuxuan Liu, Nikhil Mishra, Pieter Abbeel, Xi Chen:
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN. ICRA 2023: 7069-7075 - [c304]Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama, Aravind Rajeswaran, Jyothish Pari, Kyle Hatch, Aryan Jain, Tianhe Yu, Pieter Abbeel, Lerrel Pinto, Chelsea Finn, Abhinav Gupta:
Train Offline, Test Online: A Real Robot Learning Benchmark. ICRA 2023: 9197-9203 - [c303]Yuxuan Liu, Xi Chen, Pieter Abbeel:
Self-Supervised Instance Segmentation by Grasping. IROS 2023: 1162-1169 - [c302]Hiroshi Yoshitake, Pieter Abbeel:
The Impact of Overall Optimization on Warehouse Automation. IROS 2023: 1621-1628 - [c301]Nikhil Mishra, Pieter Abbeel, Xi Chen, Maximilian Sieb:
Convolutional Occupancy Models for Dense Packing of Complex, Novel Objects. IROS 2023: 9536-9542 - [c300]Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Josh Tenenbaum, Dale Schuurmans, Pieter Abbeel:
Learning Universal Policies via Text-Guided Video Generation. NeurIPS 2023 - [c299]Alejandro Escontrela, Ademi Adeniji, Wilson Yan, Ajay Jain, Xue Bin Peng, Ken Goldberg, Youngwoon Lee, Danijar Hafner, Pieter Abbeel:
Video Prediction Models as Rewards for Reinforcement Learning. NeurIPS 2023 - [c298]Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Kangwook Lee, Kimin Lee:
Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models. NeurIPS 2023 - [c297]Dongyoung Kim, Jinwoo Shin, Pieter Abbeel, Younggyo Seo:
Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration. NeurIPS 2023 - [c296]Hao Liu, Pieter Abbeel:
Blockwise Parallel Transformers for Large Context Models. NeurIPS 2023 - [c295]Hao Liu, Wilson Yan, Pieter Abbeel:
Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment. NeurIPS 2023 - [c294]Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Yecheng Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Tingfan Wu, Jay Vakil, Pieter Abbeel, Jitendra Malik, Dhruv Batra, Yixin Lin, Oleksandr Maksymets, Aravind Rajeswaran, Franziska Meier:
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? NeurIPS 2023 - [c293]Daiki E. Matsunaga, Jongmin Lee, Jaeseok Yoon, Stefanos Leonardos, Pieter Abbeel, Kee-Eung Kim:
AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation. NeurIPS 2023 - [c292]Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath:
Robust and Versatile Bipedal Jumping Control through Reinforcement Learning. Robotics: Science and Systems 2023 - 2022
- [c291]Abdus Salam Azad, Edward Kim, Qiancheng Wu, Kimin Lee, Ion Stoica, Pieter Abbeel, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia:
Programmatic Modeling and Generation of Real-Time Strategic Soccer Environments for Reinforcement Learning. AAAI 2022: 6028-6036 - [c290]Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch:
Frozen Pretrained Transformers as Universal Computation Engines. AAAI 2022: 7628-7636 - [c289]Ryan Hoque, Lawrence Yunliang Chen, Satvik Sharma, Karthik Dharmarajan, Brijen Thananjeyan, Pieter Abbeel, Ken Goldberg:
Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision. CoRL 2022: 368-380 - [c288]Ilija Radosavovic, Tete Xiao, Stephen James, Pieter Abbeel, Jitendra Malik, Trevor Darrell:
Real-World Robot Learning with Masked Visual Pre-training. CoRL 2022: 416-426 - [c287]Younggyo Seo, Danijar Hafner, Hao Liu, Fangchen Liu, Stephen James, Kimin Lee, Pieter Abbeel:
Masked World Models for Visual Control. CoRL 2022: 1332-1344 - [c286]John So, Amber Xie, Sunggoo Jung, Jeffrey A. Edlund, Rohan Thakker, Ali-akbar Agha-mohammadi, Pieter Abbeel, Stephen James:
Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data. CoRL 2022: 1871-1881 - [c285]Philipp Wu, Alejandro Escontrela, Danijar Hafner, Pieter Abbeel, Ken Goldberg:
DayDreamer: World Models for Physical Robot Learning. CoRL 2022: 2226-2240 - [c284]Ajay Jain, Ben Mildenhall, Jonathan T. Barron, Pieter Abbeel, Ben Poole:
Zero-Shot Text-Guided Object Generation with Dream Fields. CVPR 2022: 857-866 - [c283]Kai Chen, Rui Cao, Stephen James, Yichuan Li, Yun-Hui Liu, Pieter Abbeel, Qi Dou:
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking. ECCV (39) 2022: 533-550 - [c282]Yuxuan Liu, Nikhil Mishra, Maximilian Sieb, Yide Shentu, Pieter Abbeel, Xi Chen:
Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction. ECCV (10) 2022: 673-694 - [c281]Younggyo Seo, Kimin Lee, Fangchen Liu, Stephen James, Pieter Abbeel:
HARP: Autoregressive Latent Video Prediction with High-Fidelity Image Generator. ICIP 2022: 3943-3947 - [c280]Yuqing Du, Pieter Abbeel, Aditya Grover:
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation. ICLR 2022 - [c279]Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan, Pieter Abbeel, Michael Laskin:
Hierarchical Few-Shot Imitation with Skill Transition Models. ICLR 2022 - [c278]Xinran Liang, Katherine Shu, Kimin Lee, Pieter Abbeel:
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning. ICLR 2022 - [c277]Jongjin Park, Younggyo Seo, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee:
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning. ICLR 2022 - [c276]Wenlong Huang, Pieter Abbeel, Deepak Pathak, Igor Mordatch:
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents. ICML 2022: 9118-9147 - [c275]Litian Liang, Yaosheng Xu, Stephen McAleer, Dailin Hu, Alexander Ihler, Pieter Abbeel, Roy Fox:
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks. ICML 2022: 13285-13301 - [c274]Younggyo Seo, Kimin Lee, Stephen James, Pieter Abbeel:
Reinforcement Learning with Action-Free Pre-Training from Videos. ICML 2022: 19561-19579 - [c273]Mandi Zhao, Fangchen Liu, Kimin Lee, Pieter Abbeel:
Towards More Generalizable One-shot Visual Imitation Learning. ICRA 2022: 2434-2444 - [c272]Alejandro Escontrela, Xue Bin Peng, Wenhao Yu, Tingnan Zhang, Atil Iscen, Ken Goldberg, Pieter Abbeel:
Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions. IROS 2022: 25-32 - [c271]Sarah Young, Jyothish Pari, Pieter Abbeel, Lerrel Pinto:
Playful Interactions for Representation Learning. IROS 2022: 992-999 - [c270]Albert Zhan, Ruihan Zhao, Lerrel Pinto, Pieter Abbeel, Michael Laskin:
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation. IROS 2022: 4040-4047 - [c269]Kyle Hollins Wray, Stas Tiomkin, Mykel J. Kochenderfer, Pieter Abbeel:
Multi-Objective Policy Gradients with Topological Constraints. IROS 2022: 9034-9039 - [c268]Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel:
Deep Hierarchical Planning from Pixels. NeurIPS 2022 - [c267]Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel:
Unsupervised Reinforcement Learning with Contrastive Intrinsic Control. NeurIPS 2022 - [c266]Fangchen Liu, Hao Liu, Aditya Grover, Pieter Abbeel:
Masked Autoencoding for Scalable and Generalizable Decision Making. NeurIPS 2022 - [c265]Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Chain of Thought Imitation with Procedure Cloning. NeurIPS 2022 - [c264]Weirui Ye, Pieter Abbeel, Yang Gao:
Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions. NeurIPS 2022 - [c263]Mandi Zhao, Pieter Abbeel, Stephen James:
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning. NeurIPS 2022 - [c262]Qiyang Li, Ajay Jain, Pieter Abbeel:
AdaCat: Adaptive categorical discretization for autoregressive models. UAI 2022: 1188-1198 - 2021
- [c261]Xiaofei Wang, Kimin Lee, Kourosh Hakhamaneshi, Pieter Abbeel, Michael Laskin:
Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback. CoRL 2021: 1259-1268 - [c260]Seunghyun Lee, Younggyo Seo, Kimin Lee, Pieter Abbeel, Jinwoo Shin:
Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble. CoRL 2021: 1702-1712 - [c259]Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani:
Bottleneck Transformers for Visual Recognition. CVPR 2021: 16519-16529 - [c258]Paras Jain, Ajay Jain, Tianjun Zhang, Pieter Abbeel, Joseph Gonzalez, Ion Stoica:
Contrastive Code Representation Learning. EMNLP (1) 2021: 5954-5971 - [c257]Ajay Jain, Matthew Tancik, Pieter Abbeel:
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis. ICCV 2021: 5865-5874 - [c256]Ruihan Zhao, Kevin Lu, Pieter Abbeel, Stas Tiomkin:
Efficient Empowerment Estimation for Unsupervised Stabilization. ICLR 2021 - [c255]Nicklas Hansen, Rishabh Jangir, Yu Sun, Guillem Alenyà, Pieter Abbeel, Alexei A. Efros, Lerrel Pinto, Xiaolong Wang:
Self-Supervised Policy Adaptation during Deployment. ICLR 2021 - [c254]Donald Joseph Hejna III, Pieter Abbeel, Lerrel Pinto:
Task-Agnostic Morphology Evolution. ICLR 2021 - [c253]David Lindner, Rohin Shah, Pieter Abbeel, Anca D. Dragan:
Learning What To Do by Simulating the Past. ICLR 2021 - [c252]Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch:
Reset-Free Lifelong Learning with Skill-Space Planning. ICLR 2021 - [c251]Rui Zhao, Yang Gao, Pieter Abbeel, Volker Tresp, Wei Xu:
Mutual Information State Intrinsic Control. ICLR 2021 - [c250]Boyuan Chen, Pieter Abbeel, Deepak Pathak:
Unsupervised Learning of Visual 3D Keypoints for Control. ICML 2021: 1539-1549 - [c249]Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel:
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning. ICML 2021: 6131-6141 - [c248]Kimin Lee, Laura M. Smith, Pieter Abbeel:
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training. ICML 2021: 6152-6163 - [c247]Hao Liu, Pieter Abbeel:
APS: Active Pretraining with Successor Features. ICML 2021: 6736-6747 - [c246]Roshan Rao, Jason Liu, Robert Verkuil, Joshua Meier, John F. Canny, Pieter Abbeel, Tom Sercu, Alexander Rives:
MSA Transformer. ICML 2021: 8844-8856 - [c245]Younggyo Seo, Lili Chen, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee:
State Entropy Maximization with Random Encoders for Efficient Exploration. ICML 2021: 9443-9454 - [c244]Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin:
Decoupling Representation Learning from Reinforcement Learning. ICML 2021: 9870-9879 - [c243]Yuqing Du, Olivia Watkins, Trevor Darrell, Pieter Abbeel, Deepak Pathak:
Auto-Tuned Sim-to-Real Transfer. ICRA 2021: 1290-1296 - [c242]Zhongyu Li, Xuxin Cheng, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath:
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots. ICRA 2021: 2811-2817 - [c241]Cynthia Chen, Xin Chen, Sam Toyer, Cody Wild, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven H. Wang, Ping Luo, Stuart Russell, Pieter Abbeel, Rohin Shah:
An Empirical Investigation of Representation Learning for Imitation. NeurIPS Datasets and Benchmarks 2021 - [c240]Charles Packer, Pieter Abbeel, Joseph E. Gonzalez:
Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL. NeurIPS 2021: 2466-2477 - [c239]Olivia Watkins, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Jacob Andreas:
Teachable Reinforcement Learning via Advice Distillation. NeurIPS 2021: 6920-6933 - [c238]Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch:
Decision Transformer: Reinforcement Learning via Sequence Modeling. NeurIPS 2021: 15084-15097 - [c237]Michael Laskin, Denis Yarats, Hao Liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, Pieter Abbeel:
URLB: Unsupervised Reinforcement Learning Benchmark. NeurIPS Datasets and Benchmarks 2021 - [c236]Kimin Lee, Laura M. Smith, Anca D. Dragan, Pieter Abbeel:
B-Pref: Benchmarking Preference-Based Reinforcement Learning. NeurIPS Datasets and Benchmarks 2021 - [c235]Hao Liu, Pieter Abbeel:
Behavior From the Void: Unsupervised Active Pre-Training. NeurIPS 2021: 18459-18473 - [c234]Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Michael Laskin:
Reinforcement Learning with Latent Flow. NeurIPS 2021: 22171-22183 - [c233]Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, Yang Gao:
Mastering Atari Games with Limited Data. NeurIPS 2021: 25476-25488 - [c232]Lili Chen, Kimin Lee, Aravind Srinivas, Pieter Abbeel:
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings. NeurIPS 2021: 26779-26791 - 2020
- [c231]Wilson Yan, Ashwin Vangipuram, Pieter Abbeel, Lerrel Pinto:
Learning Predictive Representations for Deformable Objects Using Contrastive Estimation. CoRL 2020: 564-574 - [c230]Sarah Young, Dhiraj Gandhi, Shubham Tulsiani, Abhinav Gupta, Pieter Abbeel, Lerrel Pinto:
Visual Imitation Made Easy. CoRL 2020: 1992-2005 - [c229]Ignasi Clavera, Yao Fu, Pieter Abbeel:
Model-Augmented Actor-Critic: Backpropagating through Paths. ICLR 2020 - [c228]Alexander C. Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel:
Sub-policy Adaptation for Hierarchical Reinforcement Learning. ICLR 2020 - [c227]Donald J. Hejna III, Lerrel Pinto, Pieter Abbeel:
Hierarchically Decoupled Imitation For Morphological Transfer. ICML 2020: 4159-4171 - [c226]Michael Laskin, Aravind Srinivas, Pieter Abbeel:
CURL: Contrastive Unsupervised Representations for Reinforcement Learning. ICML 2020: 5639-5650 - [c225]Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan, Xi Chen:
Variable Skipping for Autoregressive Range Density Estimation. ICML 2020: 6040-6049 - [c224]Kara Liu, Thanard Kurutach, Christine Tung, Pieter Abbeel, Aviv Tamar:
Hallucinative Topological Memory for Zero-Shot Visual Planning. ICML 2020: 6259-6270 - [c223]Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak:
Planning to Explore via Self-Supervised World Models. ICML 2020: 8583-8592 - [c222]Adam Stooke, Joshua Achiam, Pieter Abbeel:
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods. ICML 2020: 9133-9143 - [c221]Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra:
Plan2Vec: Unsupervised Representation Learning by Latent Plans. L4DC 2020: 935-946 - [c220]Paras Jain, Ajay Jain, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Kurt Keutzer, Ion Stoica, Joseph Gonzalez:
Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization. MLSys 2020 - [c219]Yuqing Du, Stas Tiomkin, Emre Kiciman, Daniel Polani, Pieter Abbeel, Anca D. Dragan:
AvE: Assistance via Empowerment. NeurIPS 2020 - [c218]Scott Emmons, Ajay Jain, Michael Laskin, Thanard Kurutach, Pieter Abbeel, Deepak Pathak:
Sparse Graphical Memory for Robust Planning. NeurIPS 2020 - [c217]Jonathan Ho, Ajay Jain, Pieter Abbeel:
Denoising Diffusion Probabilistic Models. NeurIPS 2020 - [c216]Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas:
Reinforcement Learning with Augmented Data. NeurIPS 2020 - [c215]Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine:
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model. NeurIPS 2020 - [c214]Alexander C. Li, Lerrel Pinto, Pieter Abbeel:
Generalized Hindsight for Reinforcement Learning. NeurIPS 2020 - [c213]Younggyo Seo, Kimin Lee, Ignasi Clavera Gilaberte, Thanard Kurutach, Jinwoo Shin, Pieter Abbeel:
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning. NeurIPS 2020 - [c212]Yunzhi Zhang, Pieter Abbeel, Lerrel Pinto:
Automatic Curriculum Learning through Value Disagreement. NeurIPS 2020 - [c211]Laura M. Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, Sergey Levine:
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos. Robotics: Science and Systems 2020 - [c210]Yilin Wu, Wilson Yan, Thanard Kurutach, Lerrel Pinto, Pieter Abbeel:
Learning to Manipulate Deformable Objects without Demonstrations. Robotics: Science and Systems 2020 - [c209]Ajay Jain, Pieter Abbeel, Deepak Pathak:
Locally Masked Convolution for Autoregressive Models. UAI 2020: 1358-1367 - 2019
- [c208]Menglong Guo, Philipp Wu, Brent Yi, David V. Gealy, Stephen McKinley, Pieter Abbeel:
Blue Gripper: A Robust, Low-Cost, and Force-Controlled Robot Hand. CASE 2019: 1505-1510 - [c207]Yunzhi Zhang, Ignasi Clavera, Boren Tsai, Pieter Abbeel:
Asynchronous Methods for Model-Based Reinforcement Learning. CoRL 2019: 1338-1347 - [c206]Kourosh Hakhamaneshi, Nick Werblun, Pieter Abbeel, Vladimir Stojanovic:
Analog Circuit Generator based on Deep Neural Network enhanced Combinatorial Optimization. DAC 2019: 228 - [c205]Kourosh Hakhamaneshi, Nick Werblun, Pieter Abbeel, Vladimir Stojanovic:
BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks. ICCAD 2019: 1-8 - [c204]John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev, Nick Altieri, Jacob Andreas, John DeNero, Pieter Abbeel, Sergey Levine:
Guiding Policies with Language via Meta-Learning. ICLR (Poster) 2019 - [c203]Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn:
Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning. ICLR (Poster) 2019 - [c202]Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, Sergey Levine:
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow. ICLR (Poster) 2019 - [c201]Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, Pieter Abbeel:
ProMP: Proximal Meta-Policy Search. ICLR (Poster) 2019 - [c200]Rohin Shah, Dmitrii Krasheninnikov, Jordan Alexander, Pieter Abbeel, Anca D. Dragan:
Preferences Implicit in the State of the World. ICLR (Poster) 2019 - [c199]Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel:
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design. ICML 2019: 2722-2730 - [c198]Daniel Ho, Eric Liang, Xi Chen, Ion Stoica, Pieter Abbeel:
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules. ICML 2019: 2731-2741 - [c197]Friso H. Kingma, Pieter Abbeel, Jonathan Ho:
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables. ICML 2019: 3408-3417 - [c196]Rohin Shah, Noah Gundotra, Pieter Abbeel, Anca D. Dragan:
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference. ICML 2019: 5670-5679 - [c195]Marvin Zhang, Sharad Vikram, Laura M. Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine:
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning. ICML 2019: 7444-7453 - [c194]David V. Gealy, Stephen McKinley, Brent Yi, Philipp Wu, Phillip R. Downey, Greg Balke, Allan Zhao, Menglong Guo, Rachel Thomasson, Anthony Sinclair, Peter Cuellar, Zoe McCarthy, Pieter Abbeel:
Quasi-Direct Drive for Low-Cost Compliant Robotic Manipulation. ICRA 2019: 437-443 - [c193]Jianlan Luo, Eugen Solowjow, Chengtao Wen, Juan Aparicio Ojea, Alice M. Agogino, Aviv Tamar, Pieter Abbeel:
Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly. ICRA 2019: 3080-3087 - [c192]Katie Kang, Suneel Belkhale, Gregory Kahn, Pieter Abbeel, Sergey Levine:
Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight. ICRA 2019: 6008-6014 - [c191]Xinyi Ren, Jianlan Luo, Eugen Solowjow, Juan Aparicio Ojea, Abhishek Gupta, Aviv Tamar, Pieter Abbeel:
Domain Randomization for Active Pose Estimation. ICRA 2019: 7228-7234 - [c190]Tianhe Yu, Pieter Abbeel, Sergey Levine, Chelsea Finn:
One-Shot Composition of Vision-Based Skills from Demonstration. IROS 2019: 2643-2650 - [c189]Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine:
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies. NeurIPS 2019: 3681-3692 - [c188]Jonathan Ho, Evan Lohn, Pieter Abbeel:
Compression with Flows via Local Bits-Back Coding. NeurIPS 2019: 3874-3883 - [c187]Micah Carroll, Rohin Shah, Mark K. Ho, Tom Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca D. Dragan:
On the Utility of Learning about Humans for Human-AI Coordination. NeurIPS 2019: 5175-5186 - [c186]Himanshu Sahni, Toby Buckley, Pieter Abbeel, Ilya Kuzovkin:
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs. NeurIPS 2019: 5823-5833 - [c185]Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn:
Guided Meta-Policy Search. NeurIPS 2019: 9653-9664 - [c184]Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John F. Canny, Pieter Abbeel, Yun S. Song:
Evaluating Protein Transfer Learning with TAPE. NeurIPS 2019: 9686-9698 - [c183]Joshua Tobin, Wojciech Zaremba, Pieter Abbeel:
Geometry-Aware Neural Rendering. NeurIPS 2019: 11555-11565 - [c182]Coline Devin, Daniel Geng, Pieter Abbeel, Trevor Darrell, Sergey Levine:
Compositional Plan Vectors. NeurIPS 2019: 14963-14974 - [c181]Yiming Ding, Carlos Florensa, Pieter Abbeel, Mariano Phielipp:
Goal-conditioned Imitation Learning. NeurIPS 2019: 15298-15309 - [c180]Angelina Wang, Thanard Kurutach, Pieter Abbeel, Aviv Tamar:
Learning Robotic Manipulation through Visual Planning and Acting. Robotics: Science and Systems 2019 - 2018
- [c179]Igor Mordatch, Pieter Abbeel:
Emergence of Grounded Compositional Language in Multi-Agent Populations. AAAI 2018: 1495-1502 - [c178]Edward Groshev, Aviv Tamar, Maxwell Goldstein, Siddharth Srivastava, Pieter Abbeel:
Learning Generalized Reactive Policies using Deep Neural Networks. AAAI Spring Symposia 2018 - [c177]Dennis Lee, Haoran Tang, Jeffrey O. Zhang, Huazhe Xu, Trevor Darrell, Pieter Abbeel:
Modular Architecture for StarCraft II with Deep Reinforcement Learning. AIIDE 2018: 187-193 - [c176]Edward Groshev, Maxwell Goldstein, Aviv Tamar, Siddharth Srivastava, Pieter Abbeel:
Learning Generalized Reactive Policies Using Deep Neural Networks. ICAPS 2018: 408-416 - [c175]Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, Igor Mordatch:
Learning with Opponent-Learning Awareness. AAMAS 2018: 122-130 - [c174]Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel:
Model-Based Reinforcement Learning via Meta-Policy Optimization. CoRL 2018: 617-629 - [c173]Gregory Kahn, Adam Villaflor, Pieter Abbeel, Sergey Levine:
Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation. CoRL 2018: 806-816 - [c172]Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel:
PixelSNAIL: An Improved Autoregressive Generative Model. ICLR (Workshop) 2018 - [c171]Maruan Al-Shedivat, Trapit Bansal, Yura Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel:
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments. ICLR 2018 - [c170]Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman:
Meta Learning Shared Hierarchies. ICLR (Poster) 2018 - [c169]Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel:
Model-Ensemble Trust-Region Policy Optimization. ICLR (Poster) 2018 - [c168]Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel:
A Simple Neural Attentive Meta-Learner. ICLR (Poster) 2018 - [c167]Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y. Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, Marcin Andrychowicz:
Parameter Space Noise for Exploration. ICLR (Poster) 2018 - [c166]Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham M. Kakade, Igor Mordatch, Pieter Abbeel:
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines. ICLR 2018 - [c165]Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. ICLR (Workshop) 2018 - [c164]Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel:
PixelSNAIL: An Improved Autoregressive Generative Model. ICML 2018: 863-871 - [c163]John D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine:
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings. ICML 2018: 1008-1017 - [c162]Carlos Florensa, David Held, Xinyang Geng, Pieter Abbeel:
Automatic Goal Generation for Reinforcement Learning Agents. ICML 2018: 1514-1523 - [c161]Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, Sergey Levine:
Latent Space Policies for Hierarchical Reinforcement Learning. ICML 2018: 1846-1855 - [c160]Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine:
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. ICML 2018: 1856-1865 - [c159]Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn:
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control. ICML 2018: 4739-4748 - [c158]Gregory Kahn, Adam Villaflor, Bosen Ding, Pieter Abbeel, Sergey Levine:
Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation. ICRA 2018: 1-8 - [c157]Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel:
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization. ICRA 2018: 1-8 - [c156]Garrett Thomas, Melissa Chien, Aviv Tamar, Juan Aparicio Ojea, Pieter Abbeel:
Learning Robotic Assembly from CAD. ICRA 2018: 1-9 - [c155]Tianhao Zhang, Zoe McCarthy, Owen Jow, Dennis Lee, Xi Chen, Ken Goldberg, Pieter Abbeel:
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation. ICRA 2018: 1-8 - [c154]Yuxuan Liu, Abhishek Gupta, Pieter Abbeel, Sergey Levine:
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation. ICRA 2018: 1118-1125 - [c153]Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine:
Composable Deep Reinforcement Learning for Robotic Manipulation. ICRA 2018: 6244-6251 - [c152]Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel:
Overcoming Exploration in Reinforcement Learning with Demonstrations. ICRA 2018: 6292-6299 - [c151]Coline Devin, Pieter Abbeel, Trevor Darrell, Sergey Levine:
Deep Object-Centric Representations for Generalizable Robot Learning. ICRA 2018: 7111-7118 - [c150]Josh Tobin, Lukas Biewald, Rocky Duan, Marcin Andrychowicz, Ankur Handa, Vikash Kumar, Bob McGrew, Alex Ray, Jonas Schneider, Peter Welinder, Wojciech Zaremba, Pieter Abbeel:
Domain Randomization and Generative Models for Robotic Grasping. IROS 2018: 3482-3489 - [c149]Sandy H. Huang, Kush Bhatia, Pieter Abbeel, Anca D. Dragan:
Establishing Appropriate Trust via Critical States. IROS 2018: 3929-3936 - [c148]Abhishek Gupta, Russell Mendonca, Yuxuan Liu, Pieter Abbeel, Sergey Levine:
Meta-Reinforcement Learning of Structured Exploration Strategies. NeurIPS 2018: 5307-5316 - [c147]Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, Pieter Abbeel:
Evolved Policy Gradients. NeurIPS 2018: 5405-5414 - [c146]Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J. Russell, Pieter Abbeel:
Learning Plannable Representations with Causal InfoGAN. NeurIPS 2018: 8747-8758 - [c145]Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever:
The Importance of Sampling inMeta-Reinforcement Learning. NeurIPS 2018: 9300-9310 - [c144]Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, Pieter Abbeel:
Asymmetric Actor Critic for Image-Based Robot Learning. Robotics: Science and Systems 2018 - [c143]Tianhe Yu, Chelsea Finn, Sudeep Dasari, Annie Xie, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. Robotics: Science and Systems 2018 - 2017
- [c142]Dylan Hadfield-Menell, Anca D. Dragan, Pieter Abbeel, Stuart Russell:
The Off-Switch Game. AAAI Workshops 2017 - [c141]Markus Wulfmeier, Ingmar Posner, Pieter Abbeel:
Mutual Alignment Transfer Learning. CoRL 2017: 281-290 - [c140]Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Visual Imitation Learning via Meta-Learning. CoRL 2017: 357-368 - [c139]Carlos Florensa, David Held, Markus Wulfmeier, Michael Zhang, Pieter Abbeel:
Reverse Curriculum Generation for Reinforcement Learning. CoRL 2017: 482-495 - [c138]Abhishek Gupta, Coline Devin, Yuxuan Liu, Pieter Abbeel, Sergey Levine:
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning. ICLR (Poster) 2017 - [c137]Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel:
Variational Lossy Autoencoder. ICLR (Poster) 2017 - [c136]Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine:
Generalizing Skills with Semi-Supervised Reinforcement Learning. ICLR (Poster) 2017 - [c135]Carlos Florensa, Yan Duan, Pieter Abbeel:
Stochastic Neural Networks for Hierarchical Reinforcement Learning. ICLR (Poster) 2017 - [c134]Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel:
Adversarial Attacks on Neural Network Policies. ICLR (Workshop) 2017 - [c133]Alex X. Lee, Sergey Levine, Pieter Abbeel:
Learning Visual Servoing with Deep Features and Fitted Q-Iteration. ICLR (Poster) 2017 - [c132]Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever:
Third Person Imitation Learning. ICLR (Poster) 2017 - [c131]Joshua Achiam, David Held, Aviv Tamar, Pieter Abbeel:
Constrained Policy Optimization. ICML 2017: 22-31 - [c130]Chelsea Finn, Pieter Abbeel, Sergey Levine:
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017: 1126-1135 - [c129]Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine:
Reinforcement Learning with Deep Energy-Based Policies. ICML 2017: 1352-1361 - [c128]Nikhil Mishra, Pieter Abbeel, Igor Mordatch:
Prediction and Control with Temporal Segment Models. ICML 2017: 2459-2468 - [c127]Aviv Tamar, Garrett Thomas, Tianhao Zhang, Sergey Levine, Pieter Abbeel:
Learning from the hindsight plan - Episodic MPC improvement. ICRA 2017: 336-343 - [c126]Marvin Zhang, Xinyang Geng, Jonathan Bruce, Ken Caluwaerts, Massimo Vespignani, Vytas SunSpiral, Pieter Abbeel, Sergey Levine:
Deep reinforcement learning for tensegrity robot locomotion. ICRA 2017: 634-641 - [c125]Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, Sergey Levine:
Combining self-supervised learning and imitation for vision-based rope manipulation. ICRA 2017: 2146-2153 - [c124]Coline Devin, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Sergey Levine:
Learning modular neural network policies for multi-task and multi-robot transfer. ICRA 2017: 2169-2176 - [c123]Gregory Kahn, Tianhao Zhang, Sergey Levine, Pieter Abbeel:
PLATO: Policy learning using adaptive trajectory optimization. ICRA 2017: 3342-3349 - [c122]William Montgomery, Anurag Ajay, Chelsea Finn, Pieter Abbeel, Sergey Levine:
Reset-free guided policy search: Efficient deep reinforcement learning with stochastic initial states. ICRA 2017: 3373-3380 - [c121]David Held, Zoe McCarthy, Michael Zhang, Fred Shentu, Pieter Abbeel:
Probabilistically safe policy transfer. ICRA 2017: 5798-5805 - [c120]Dylan Hadfield-Menell, Anca D. Dragan, Pieter Abbeel, Stuart Russell:
The Off-Switch Game. IJCAI 2017: 220-227 - [c119]Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel:
Value Iteration Networks. IJCAI 2017: 4949-4953 - [c118]Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, Pieter Abbeel:
Domain randomization for transferring deep neural networks from simulation to the real world. IROS 2017: 23-30 - [c117]Ignasi Clavera, David Held, Pieter Abbeel:
Policy transfer via modularity and reward guiding. IROS 2017: 1537-1544 - [c116]Arjun Singh, Sergey Karayev, Kevin Gutowski, Pieter Abbeel:
Gradescope: A Fast, Flexible, and Fair System for Scalable Assessment of Handwritten Work. L@S 2017: 81-88 - [c115]Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba:
One-Shot Imitation Learning. NIPS 2017: 1087-1098 - [c114]Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel:
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning. NIPS 2017: 2753-2762 - [c113]Marcin Andrychowicz, Dwight Crow, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, Wojciech Zaremba:
Hindsight Experience Replay. NIPS 2017: 5048-5058 - [c112]Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, Igor Mordatch:
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. NIPS 2017: 6379-6390 - [c111]Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart J. Russell, Anca D. Dragan:
Inverse Reward Design. NIPS 2017: 6765-6774 - [c110]Sandy H. Huang, David Held, Pieter Abbeel, Anca D. Dragan:
Enabling Robots to Communicate Their Objectives. Robotics: Science and Systems 2017 - [c109]Ming Jin, Andreas C. Damianou, Pieter Abbeel, Costas J. Spanos:
Inverse Reinforcement Learning via Deep Gaussian Process. UAI 2017 - 2016
- [c108]Chelsea Finn, Sergey Levine, Pieter Abbeel:
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization. ICML 2016: 49-58 - [c107]Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel:
Benchmarking Deep Reinforcement Learning for Continuous Control. ICML 2016: 1329-1338 - [c106]Igor Mordatch, Nikhil Mishra, Clemens Eppner, Pieter Abbeel:
Combining model-based policy search with online model learning for control of physical humanoids. ICRA 2016: 242-248 - [c105]Rohan Chitnis, Dylan Hadfield-Menell, Abhishek Gupta, Siddharth Srivastava, Edward Groshev, Christopher Lin, Pieter Abbeel:
Guided search for task and motion plans using learned heuristics. ICRA 2016: 447-454 - [c104]Chris Xie, Sachin Patil, Teodor Mihai Moldovan, Sergey Levine, Pieter Abbeel:
Model-based reinforcement learning with parametrized physical models and optimism-driven exploration. ICRA 2016: 504-511 - [c103]Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel:
Deep spatial autoencoders for visuomotor learning. ICRA 2016: 512-519 - [c102]Marvin Zhang, Zoe McCarthy, Chelsea Finn, Sergey Levine, Pieter Abbeel:
Learning deep neural network policies with continuous memory states. ICRA 2016: 520-527 - [c101]Tianhao Zhang, Gregory Kahn, Sergey Levine, Pieter Abbeel:
Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search. ICRA 2016: 528-535 - [c100]Adithyavairavan Murali, Animesh Garg, Sanjay Krishnan, Florian T. Pokorny, Pieter Abbeel, Trevor Darrell, Ken Goldberg:
TSC-DL: Unsupervised trajectory segmentation of multi-modal surgical demonstrations with Deep Learning. ICRA 2016: 4150-4157 - [c99]Karol Hausman, Gregory Kahn, Sachin Patil, Jörg Müller, Ken Goldberg, Pieter Abbeel, Gaurav S. Sukhatme:
Occlusion-aware multi-robot 3D tracking. IROS 2016: 1863-1870 - [c98]Abhishek Gupta, Clemens Eppner, Sergey Levine, Pieter Abbeel:
Learning dexterous manipulation for a soft robotic hand from human demonstrations. IROS 2016: 3786-3793 - [c97]Justin Fu, Sergey Levine, Pieter Abbeel:
One-shot learning of manipulation skills with online dynamics adaptation and neural network priors. IROS 2016: 4019-4026 - [c96]Dylan Hadfield-Menell, Christopher Lin, Rohan Chitnis, Stuart Russell, Pieter Abbeel:
Sequential quadratic programming for task plan optimization. IROS 2016: 5040-5047 - [c95]Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel:
VIME: Variational Information Maximizing Exploration. NIPS 2016: 1109-1117 - [c94]Jeremy B. Maitin-Shepard, Viren Jain, Michal Januszewski, Peter Li, Pieter Abbeel:
Combinatorial Energy Learning for Image Segmentation. NIPS 2016: 1966-1974 - [c93]Aviv Tamar, Sergey Levine, Pieter Abbeel, Yi Wu, Garrett Thomas:
Value Iteration Networks. NIPS 2016: 2146-2154 - [c92]Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel:
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. NIPS 2016: 2172-2180 - [c91]Dylan Hadfield-Menell, Stuart Russell, Pieter Abbeel, Anca D. Dragan:
Cooperative Inverse Reinforcement Learning. NIPS 2016: 3909-3917 - [c90]Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel:
Backprop KF: Learning Discriminative Deterministic State Estimators. NIPS 2016: 4376-4384 - [c89]Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Pieter Abbeel, Sergey Levine, Kate Saenko, Trevor Darrell:
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints. WAFR 2016: 688-703 - [c88]John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, Pieter Abbeel:
High-Dimensional Continuous Control Using Generalized Advantage Estimation. ICLR (Poster) 2016 - 2015
- [c87]Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell:
Tractability of Planning with Loops. AAAI 2015: 3393-3401 - [c86]Michael Laskey, Jeffrey Mahler, Zoe McCarthy, Florian T. Pokorny, Sachin Patil, Jur P. van den Berg, Danica Kragic, Pieter Abbeel, Ken Goldberg:
Multi-armed bandit models for 2D grasp planning with uncertainty. CASE 2015: 572-579 - [c85]Stephen McKinley, Animesh Garg, Siddarth Sen, Rishi Kapadia, Adithyavairavan Murali, Kirk A. Nichols, Susan Lim, Sachin Patil, Pieter Abbeel, Allison M. Okamura, Ken Goldberg:
A single-use haptic palpation probe for locating subcutaneous blood vessels in robot-assisted minimally invasive surgery. CASE 2015: 1151-1158 - [c84]Berk Çalli, Arjun Singh, Aaron Walsman, Siddhartha S. Srinivasa, Pieter Abbeel, Aaron M. Dollar:
The YCB object and Model set: Towards common benchmarks for manipulation research. ICAR 2015: 510-517 - [c83]Karthik S. Narayan, Ali Punjani, Pieter Abbeel:
Alpha-Beta Divergences Discover Micro and Macro Structures in Data. ICML 2015: 796-804 - [c82]John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, Philipp Moritz:
Trust Region Policy Optimization. ICML 2015: 1889-1897 - [c81]Sergey Levine, Nolan Wagener, Pieter Abbeel:
Learning contact-rich manipulation skills with guided policy search. ICRA 2015: 156-163 - [c80]Alex X. Lee, Henry Lu, Abhishek Gupta, Sergey Levine, Pieter Abbeel:
Learning force-based manipulation of deformable objects from multiple demonstrations. ICRA 2015: 177-184 - [c79]Alex X. Lee, Max A. Goldstein, Shane T. Barratt, Pieter Abbeel:
A non-rigid point and normal registration algorithm with applications to learning from demonstrations. ICRA 2015: 935-942 - [c78]Adithyavairavan Murali, Siddarth Sen, Ben Kehoe, Animesh Garg, Seth McFarland, Sachin Patil, W. Douglas Boyd, Susan Lim, Pieter Abbeel, Kenneth Y. Goldberg:
Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms. ICRA 2015: 1202-1209 - [c77]Nikita Kitaev, Igor Mordatch, Sachin Patil, Pieter Abbeel:
Physics-based trajectory optimization for grasping in cluttered environments. ICRA 2015: 3102-3109 - [c76]Ali Punjani, Pieter Abbeel:
Deep learning helicopter dynamics models. ICRA 2015: 3223-3230 - [c75]Dylan Hadfield-Menell, Alex X. Lee, Chelsea Finn, Eric Tzeng, Sandy H. Huang, Pieter Abbeel:
Beyond lowest-warping cost action selection in trajectory transfer. ICRA 2015: 3231-3238 - [c74]Teodor Mihai Moldovan, Sergey Levine, Michael I. Jordan, Pieter Abbeel:
Optimism-driven exploration for nonlinear systems. ICRA 2015: 3239-3246 - [c73]Karthik S. Narayan, James Sha, Arjun Singh, Pieter Abbeel:
Range sensor and silhouette fusion for high-quality 3D Scanning. ICRA 2015: 3617-3624 - [c72]Christopher Xie, Jur P. van den Berg, Sachin Patil, Pieter Abbeel:
Toward asymptotically optimal motion planning for kinodynamic systems using a two-point boundary value problem solver. ICRA 2015: 4187-4194 - [c71]Gregory Kahn, Peter Sujan, Sachin Patil, Shaunak D. Bopardikar, Julian Ryde, Kenneth Y. Goldberg, Pieter Abbeel:
Active exploration using trajectory optimization for robotic grasping in the presence of occlusions. ICRA 2015: 4783-4790 - [c70]Jeffrey Mahler, Sachin Patil, Ben Kehoe, Jur van den Berg, Matei T. Ciocarlie, Pieter Abbeel, Ken Goldberg:
GP-GPIS-OPT: Grasp planning with shape uncertainty using Gaussian process implicit surfaces and Sequential Convex Programming. ICRA 2015: 4919-4926 - [c69]Sandy H. Huang, Jia Pan, George Mulcaire, Pieter Abbeel:
Leveraging appearance priors in non-rigid registration, with application to manipulation of deformable objects. IROS 2015: 878-885 - [c68]Karthik S. Narayan, Pieter Abbeel:
Optimized color models for high-quality 3D scanning. IROS 2015: 2503-2510 - [c67]Dylan Hadfield-Menell, Edward Groshev, Rohan Chitnis, Pieter Abbeel:
Modular task and motion planning in belief space. IROS 2015: 4991-4998 - [c66]Alex X. Lee, Abhishek Gupta, Henry Lu, Sergey Levine, Pieter Abbeel:
Learning from multiple demonstrations using trajectory-aware non-rigid registration with applications to deformable object manipulation. IROS 2015: 5265-5272 - [c65]Weiqiao Han, Sergey Levine, Pieter Abbeel:
Learning compound multi-step controllers under unknown dynamics. IROS 2015: 6435-6442 - [c64]Sanjay Krishnan, Animesh Garg, Sachin Patil, Colin Lea, Gregory D. Hager, Pieter Abbeel, Ken Goldberg:
Transition State Clustering: Unsupervised Surgical Trajectory Segmentation for Robot Learning. ISRR (2) 2015: 91-110 - [c63]John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel:
Gradient Estimation Using Stochastic Computation Graphs. NIPS 2015: 3528-3536 - [c62]Benjamin Charrow, Gregory Kahn, Sachin Patil, Sikang Liu, Ken Goldberg, Pieter Abbeel, Nathan Michael, Vijay Kumar:
Information-Theoretic Planning with Trajectory Optimization for Dense 3D Mapping. Robotics: Science and Systems 2015 - 2014
- [c61]Jeffrey Mahler, Sanjay Krishnan, Michael Laskey, Siddarth Sen, Adithyavairavan Murali, Ben Kehoe, Sachin Patil, Jiannan Wang, Mike Franklin, Pieter Abbeel, Kenneth Y. Goldberg:
Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression. CASE 2014: 532-539 - [c60]Samaneh Azadi, Jeremy Maitin-Shepard, Pieter Abbeel:
Optimization-Based Artifact Correction for Electron Microscopy Image Stacks. ECCV (2) 2014: 219-235 - [c59]Arjun Singh, James Sha, Karthik S. Narayan, Tudor Achim, Pieter Abbeel:
BigBIRD: A large-scale 3D database of object instances. ICRA 2014: 509-516 - [c58]Siddharth Srivastava, Eugene Fang, Lorenzo Riano, Rohan Chitnis, Stuart Russell, Pieter Abbeel:
Combined task and motion planning through an extensible planner-independent interface layer. ICRA 2014: 639-646 - [c57]Ben Kehoe, Gregory Kahn, Jeffrey Mahler, Jonathan Kim, Alex X. Lee, Anna Lee, Keisuke Nakagawa, Sachin Patil, W. Douglas Boyd, Pieter Abbeel, Kenneth Y. Goldberg:
Autonomous multilateral debridement with the Raven surgical robot. ICRA 2014: 1432-1439 - [c56]Jia Pan, Zhuo Chen, Pieter Abbeel:
Predicting initialization effectiveness for trajectory optimization. ICRA 2014: 5183-5190 - [c55]Yan Duan, Sachin Patil, John Schulman, Kenneth Y. Goldberg, Pieter Abbeel:
Planning locally optimal, curvature-constrained trajectories in 3D using sequential convex optimization. ICRA 2014: 5889-5895 - [c54]Sachin Patil, Yan Duan, John Schulman, Ken Goldberg, Pieter Abbeel:
Gaussian belief space planning with discontinuities in sensing domains. ICRA 2014: 6483-6490 - [c53]Alex X. Lee, Sandy H. Huang, Dylan Hadfield-Menell, Eric Tzeng, Pieter Abbeel:
Unifying scene registration and trajectory optimization for learning from demonstrations with application to manipulation of deformable objects. IROS 2014: 4402-4407 - [c52]Sergey Levine, Pieter Abbeel:
Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics. NIPS 2014: 1071-1079 - [c51]Sachin Patil, Gregory Kahn, Michael Laskey, John Schulman, Ken Goldberg, Pieter Abbeel:
Scaling up Gaussian Belief Space Planning Through Covariance-Free Trajectory Optimization and Automatic Differentiation. WAFR 2014: 515-533 - [c50]Sachin Patil, Jia Pan, Pieter Abbeel, Ken Goldberg:
Planning Curvature and Torsion Constrained Ribbons in 3D with Application to Intracavitary Brachytherapy. WAFR 2014: 535-552 - 2013
- [c49]Animesh Garg, Sachin Patil, Timmy Siauw, J. Adam M. Cunha, I-Chow Hsu, Pieter Abbeel, Jean Pouliot, Ken Goldberg:
An algorithm for computing customized 3D printed implants with curvature constrained channels for enhancing intracavitary brachytherapy radiation delivery. CASE 2013: 466-473 - [c48]Vo Doan Tat Thang, Svetoslav Kolev, Huynh Ngoc Anh, Zhang Chao, Travis L. Massey, Pieter Abbeel, Michel M. Maharbiz, Hirotaka Sato:
Insect-machine hybrid system. EMBC 2013: 2816-2819 - [c47]John Schulman, Alex X. Lee, Jonathan Ho, Pieter Abbeel:
Tracking deformable objects with point clouds. ICRA 2013: 1130-1137 - [c46]Sergio Guadarrama, Lorenzo Riano, Dave Golland, Daniel Göhring, Yangqing Jia, Dan Klein, Pieter Abbeel, Trevor Darrell:
Grounding spatial relations for human-robot interaction. IROS 2013: 1640-1647 - [c45]Ziang Xie, Arjun Singh, Justin Uang, Karthik S. Narayan, Pieter Abbeel:
Multimodal blending for high-accuracy instance recognition. IROS 2013: 2214-2221 - [c44]John Schulman, Ankush Gupta, Sibi Venkatesan, Mallory Tayson-Frederick, Pieter Abbeel:
A case study of trajectory transfer through non-rigid registration for a simplified suturing scenario. IROS 2013: 4111-4117 - [c43]Alex X. Lee, Yan Duan, Sachin Patil, John Schulman, Zoe McCarthy, Jur van den Berg, Ken Goldberg, Pieter Abbeel:
Sigma hulls for Gaussian belief space planning for imprecise articulated robots amid obstacles. IROS 2013: 5660-5667 - [c42]John Schulman, Jonathan Ho, Cameron Lee, Pieter Abbeel:
Learning from Demonstrations Through the Use of Non-rigid Registration. ISRR 2013: 339-354 - [c41]John Schulman, Jonathan Ho, Alex X. Lee, Ibrahim Awwal, Henry Bradlow, Pieter Abbeel:
Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization. Robotics: Science and Systems 2013 - 2012
- [c40]Teodor Mihai Moldovan, Pieter Abbeel:
Safe Exploration in Markov Decision Processes . ICML 2012 - [c39]Jie Tang, Stephen Miller, Arjun Singh, Pieter Abbeel:
A textured object recognition pipeline for color and depth image data. ICRA 2012: 3467-3474 - [c38]Dmitry Berenson, Pieter Abbeel, Ken Goldberg:
A robot path planning framework that learns from experience. ICRA 2012: 3671-3678 - [c37]Fernando Garcia Bermudez, Ryan C. Julian, Duncan W. Haldane, Pieter Abbeel, Ronald S. Fearing:
Performance analysis and terrain classification for a legged robot over rough terrain. IROS 2012: 513-519 - [c36]Karthik Lakshmanan, Apoorva Sachdev, Ziang Xie, Dmitry Berenson, Ken Goldberg, Pieter Abbeel:
A Constraint-Aware Motion Planning Algorithm for Robotic Folding of Clothes. ISER 2012: 547-562 - [c35]Teodor Mihai Moldovan, Pieter Abbeel:
Risk Aversion in Markov Decision Processes via Near Optimal Chernoff Bounds. NIPS 2012: 3140-3148 - [c34]Pieter Abbeel:
Machine Learning for Robotics. ECML/PKDD (1) 2012: 1 - [c33]Timothy Hunter, Pieter Abbeel, Alexandre M. Bayen:
The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data. WAFR 2012: 591-607 - 2011
- [c32]Timothy Hunter, Teodor Mihai Moldovan, Matei Zaharia, Samy Merzgui, Justin Ma, Michael J. Franklin, Pieter Abbeel, Alexandre M. Bayen:
Scaling the mobile millennium system in the cloud. SoCC 2011: 28 - [c31]Shervin Javdani, Sameep Tandon, Jie Tang, James F. O'Brien, Pieter Abbeel:
Modeling and perception of deformable one-dimensional objects. ICRA 2011: 1607-1614 - [c30]Marco F. Cusumano-Towner, Arjun Singh, Stephen Miller, James F. O'Brien, Pieter Abbeel:
Bringing clothing into desired configurations with limited perception. ICRA 2011: 3893-3900 - [c29]Stephen Miller, Mario Fritz, Trevor Darrell, Pieter Abbeel:
Parametrized shape models for clothing. ICRA 2011: 4861-4868 - [c28]Leonard Jaillet, Judy Hoffman, Jur van den Berg, Pieter Abbeel, Josep M. Porta, Kenneth Y. Goldberg:
EG-RRT: Environment-guided random trees for kinodynamic motion planning with uncertainty and obstacles. IROS 2011: 2646-2652 - [c27]Ping Chuan Wang, Stephen Miller, Mario Fritz, Trevor Darrell, Pieter Abbeel:
Perception for the manipulation of socks. IROS 2011: 4877-4884 - [c26]John D. Schulman, Ken Goldberg, Pieter Abbeel:
Grasping and Fixturing as Submodular Coverage Problems. ISRR 2011: 571-583 - 2010
- [c25]Jie Tang, Arjun Singh, Nimbus Goehausen, Pieter Abbeel:
Parameterized maneuver learning for autonomous helicopter flight. ICRA 2010: 1142-1148 - [c24]Jur van den Berg, Stephen Miller, Daniel Duckworth, Humphrey Hu, Andrew Wan, Xiao-Yu Fu, Kenneth Y. Goldberg, Pieter Abbeel:
Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations. ICRA 2010: 2074-2081 - [c23]Jeremy Maitin-Shepard, Marco F. Cusumano-Towner, Jinna Lei, Pieter Abbeel:
Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding. ICRA 2010: 2308-2315 - [c22]Pål Johan From, Jan Tommy Gravdahl, Pieter Abbeel:
On the influence of ship motion prediction accuracy on motion planning and control of robotic manipulators on seaborne platforms. ICRA 2010: 5281-5288 - [c21]Ryan Herring, Aude Hofleitner, Pieter Abbeel, Alexandre M. Bayen:
Estimating arterial traffic conditions using sparse probe data. ITSC 2010: 929-936 - [c20]Jie Tang, Pieter Abbeel:
On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient. NIPS 2010: 1000-1008 - [c19]Jur van den Berg, Pieter Abbeel, Kenneth Y. Goldberg:
LQG-MP: Optimized Path Planning for Robots with Motion Uncertainty and Imperfect State Information. Robotics: Science and Systems 2010 - [c18]Jur van den Berg, Sachin Patil, Ron Alterovitz, Pieter Abbeel, Kenneth Y. Goldberg:
LQG-Based Planning, Sensing, and Control of Steerable Needles. WAFR 2010: 373-389 - [c17]Jur van den Berg, Stephen Miller, Kenneth Y. Goldberg, Pieter Abbeel:
Gravity-Based Robotic Cloth Folding. WAFR 2010: 409-424 - 2008
- [c16]Adam Coates, Pieter Abbeel, Andrew Y. Ng:
Learning for control from multiple demonstrations. ICML 2008: 144-151 - [c15]Pieter Abbeel, Dmitri Dolgov, Andrew Y. Ng, Sebastian Thrun:
Apprenticeship learning for motion planning with application to parking lot navigation. IROS 2008: 1083-1090 - [c14]Pieter Abbeel, Adam Coates, Timothy Hunter, Andrew Y. Ng:
Autonomous Autorotation of an RC Helicopter. ISER 2008: 385-394 - 2007
- [c13]J. Zico Kolter, Pieter Abbeel, Andrew Y. Ng:
Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion. NIPS 2007: 769-776 - 2006
- [c12]Su-In Lee, Honglak Lee, Pieter Abbeel, Andrew Y. Ng:
Efficient L1 Regularized Logistic Regression. AAAI 2006: 401-408 - [c11]Pieter Abbeel, Morgan Quigley, Andrew Y. Ng:
Using inaccurate models in reinforcement learning. ICML 2006: 1-8 - [c10]Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng:
An Application of Reinforcement Learning to Aerobatic Helicopter Flight. NIPS 2006: 1-8 - [c9]Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller:
Max-margin classification of incomplete data. NIPS 2006: 233-240 - 2005
- [c8]Pieter Abbeel, Andrew Y. Ng:
Exploration and apprenticeship learning in reinforcement learning. ICML 2005: 1-8 - [c7]Pieter Abbeel, Varun Ganapathi, Andrew Y. Ng:
Learning vehicular dynamics, with application to modeling helicopters. NIPS 2005: 1-8 - [c6]Pieter Abbeel, Adam Coates, Michael Montemerlo, Andrew Y. Ng, Sebastian Thrun:
Discriminative Training of Kalman Filters. Robotics: Science and Systems 2005: 289-296 - [c5]Pieter Abbeel, Daphne Koller, Andrew Y. Ng:
Learning Factor Graphs in Polynomial Time & Sample Complexity. UAI 2005: 1-9 - 2004
- [c4]Pieter Abbeel, Andrew Y. Ng:
Apprenticeship learning via inverse reinforcement learning. ICML 2004 - [c3]Pieter Abbeel, Andrew Y. Ng:
Learning first-order Markov models for control. NIPS 2004: 1-8 - 2003
- [c2]Benjamin Taskar, Ming Fai Wong, Pieter Abbeel, Daphne Koller:
Link Prediction in Relational Data. NIPS 2003: 659-666 - 2002
- [c1]Benjamin Taskar, Pieter Abbeel, Daphne Koller:
Discriminative Probabilistic Models for Relational Data. UAI 2002: 485-492
Editorship
- 2020
- [e2]Ken Goldberg, Pieter Abbeel, Kostas E. Bekris, Lauren Miller:
Algorithmic Foundations of Robotics XII, Proceedings of the Twelfth Workshop on the Algorithmic Foundations of Robotics, WAFR 2016, San Francisco, California, USA, December 18-20, 2016. Springer Proceedings in Advanced Robotics 13, Springer 2020, ISBN 978-3-030-43088-7 [contents] - 2011
- [e1]Hugh F. Durrant-Whyte, Nicholas Roy, Pieter Abbeel:
Robotics: Science and Systems VII, University of Southern California, Los Angeles, CA, USA, June 27-30, 2011. 2011, ISBN 978-0-262-51779-9 [contents]
Reference Works
- 2017
- [r4]Adam Coates, Pieter Abbeel, Andrew Y. Ng:
Autonomous Helicopter Flight Using Reinforcement Learning. Encyclopedia of Machine Learning and Data Mining 2017: 75-85 - [r3]Pieter Abbeel, Andrew Y. Ng:
Inverse Reinforcement Learning. Encyclopedia of Machine Learning and Data Mining 2017: 678-682 - 2010
- [r2]Adam Coates, Pieter Abbeel, Andrew Y. Ng:
Autonomous Helicopter Flight Using Reinforcement Learning. Encyclopedia of Machine Learning 2010: 53-61 - [r1]Pieter Abbeel, Andrew Y. Ng:
Inverse Reinforcement Learning. Encyclopedia of Machine Learning 2010: 554-558
Informal and Other Publications
- 2024
- [i334]Chuan Wen, Xingyu Lin, John So, Kai Chen, Qi Dou, Yang Gao, Pieter Abbeel:
Any-point Trajectory Modeling for Policy Learning. CoRR abs/2401.00025 (2024) - [i333]Jakub Grudzien Kuba, Masatoshi Uehara, Pieter Abbeel, Sergey Levine:
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization. CoRR abs/2401.05442 (2024) - [i332]Jianlan Luo, Charles Xu, Fangchen Liu, Liam Tan, Zipeng Lin, Jeffrey Wu, Pieter Abbeel, Sergey Levine:
FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning. CoRR abs/2401.08553 (2024) - [i331]Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath:
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control. CoRR abs/2401.16889 (2024) - [i330]Hao Liu, Wilson Yan, Matei Zaharia, Pieter Abbeel:
World Model on Million-Length Video And Language With Blockwise RingAttention. CoRR abs/2402.08268 (2024) - [i329]Alexandra Souly, Qingyuan Lu, Dillon Bowen, Tu Trinh, Elvis Hsieh, Sana Pandey, Pieter Abbeel, Justin Svegliato, Scott Emmons, Olivia Watkins, Sam Toyer:
A StrongREJECT for Empty Jailbreaks. CoRR abs/2402.10260 (2024) - [i328]Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine:
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings. CoRR abs/2402.17135 (2024) - [i327]Sherry Yang, Jacob C. Walker, Jack Parker-Holder, Yilun Du, Jake Bruce, André Barreto, Pieter Abbeel, Dale Schuurmans:
Video as the New Language for Real-World Decision Making. CoRR abs/2402.17139 (2024) - [i326]Toru Lin, Zhao-Heng Yin, Haozhi Qi, Pieter Abbeel, Jitendra Malik:
Twisting Lids Off with Two Hands. CoRR abs/2403.02338 (2024) - [i325]Fangchen Liu, Kuan Fang, Pieter Abbeel, Sergey Levine:
MOKA: Open-Vocabulary Robotic Manipulation through Mark-Based Visual Prompting. CoRR abs/2403.03174 (2024) - [i324]Nikhil Mishra, Maximilian Sieb, Pieter Abbeel, Xi Chen:
Closing the Visual Sim-to-Real Gap with Object-Composable NeRFs. CoRR abs/2403.04114 (2024) - [i323]Carmelo Sferrazza, Dun-Ming Huang, Xingyu Lin, Youngwoon Lee, Pieter Abbeel:
HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation. CoRR abs/2403.10506 (2024) - [i322]Yide Shentu, Philipp Wu, Aravind Rajeswaran, Pieter Abbeel:
From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control. CoRR abs/2405.04798 (2024) - [i321]Huiwon Jang, Dongyoung Kim, Junsu Kim, Jinwoo Shin, Pieter Abbeel, Younggyo Seo:
Visual Representation Learning with Stochastic Frame Prediction. CoRR abs/2406.07398 (2024) - [i320]Vint Lee, Chun Deng, Leena Elzeiny, Pieter Abbeel, John Wawrzynek:
Chip Placement with Diffusion. CoRR abs/2407.12282 (2024) - [i319]Zhao-Heng Yin, Pieter Abbeel:
Offline Imitation Learning Through Graph Search and Retrieval. CoRR abs/2407.15403 (2024) - [i318]Philipp Wu, Kourosh Hakhamaneshi, Yuqing Du, Igor Mordatch, Aravind Rajeswaran, Pieter Abbeel:
Semi-Supervised One-Shot Imitation Learning. CoRR abs/2408.05285 (2024) - 2023
- [i317]Yilun Du, Mengjiao Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, Pieter Abbeel:
Learning Universal Policies via Text-Guided Video Generation. CoRR abs/2302.00111 (2023) - [i316]Hao Liu, Wilson Yan, Pieter Abbeel:
Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment. CoRR abs/2302.00902 (2023) - [i315]Younggyo Seo, Junsu Kim, Stephen James, Kimin Lee, Jinwoo Shin, Pieter Abbeel:
Multi-View Masked World Models for Visual Robotic Manipulation. CoRR abs/2302.02408 (2023) - [i314]Hao Liu, Carmelo Sferrazza, Pieter Abbeel:
Chain of Hindsight Aligns Language Models with Feedback. CoRR abs/2302.02676 (2023) - [i313]Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel:
Controllability-Aware Unsupervised Skill Discovery. CoRR abs/2302.05103 (2023) - [i312]Tianjun Zhang, Fangchen Liu, Justin Wong, Pieter Abbeel, Joseph E. Gonzalez:
The Wisdom of Hindsight Makes Language Models Better Instruction Followers. CoRR abs/2302.05206 (2023) - [i311]Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, Jacob Andreas:
Guiding Pretraining in Reinforcement Learning with Large Language Models. CoRR abs/2302.06692 (2023) - [i310]Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath:
Robust and Versatile Bipedal Jumping Control through Multi-Task Reinforcement Learning. CoRR abs/2302.09450 (2023) - [i309]Kimin Lee, Hao Liu, Moonkyung Ryu, Olivia Watkins, Yuqing Du, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Shixiang Shane Gu:
Aligning Text-to-Image Models using Human Feedback. CoRR abs/2302.12192 (2023) - [i308]Changyeon Kim, Jongjin Park, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee:
Preference Transformer: Modeling Human Preferences using Transformers for RL. CoRR abs/2303.00957 (2023) - [i307]Sherry Yang, Ofir Nachum, Yilun Du, Jason Wei, Pieter Abbeel, Dale Schuurmans:
Foundation Models for Decision Making: Problems, Methods, and Opportunities. CoRR abs/2303.04129 (2023) - [i306]Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Yecheng Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Pieter Abbeel, Jitendra Malik, Dhruv Batra, Yixin Lin, Oleksandr Maksymets, Aravind Rajeswaran, Franziska Meier:
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? CoRR abs/2303.18240 (2023) - [i305]Kevin Zakka, Laura M. Smith, Nimrod Gileadi, Taylor A. Howell, Xue Bin Peng, Sumeet Singh, Yuval Tassa, Pete Florence, Andy Zeng, Pieter Abbeel:
RoboPianist: A Benchmark for High-Dimensional Robot Control. CoRR abs/2304.04150 (2023) - [i304]Yuxuan Liu, Nikhil Mishra, Pieter Abbeel, Xi Chen:
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN. CoRR abs/2305.01910 (2023) - [i303]Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran:
Masked Trajectory Models for Prediction, Representation, and Control. CoRR abs/2305.02968 (2023) - [i302]Yuxuan Liu, Xi Chen, Pieter Abbeel:
Self-Supervised Instance Segmentation by Grasping. CoRR abs/2305.06305 (2023) - [i301]Alejandro Escontrela, Ademi Adeniji, Wilson Yan, Ajay Jain, Xue Bin Peng, Ken Goldberg, Youngwoon Lee, Danijar Hafner, Pieter Abbeel:
Video Prediction Models as Rewards for Reinforcement Learning. CoRR abs/2305.14343 (2023) - [i300]Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song:
The False Promise of Imitating Proprietary LLMs. CoRR abs/2305.15717 (2023) - [i299]Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Kangwook Lee, Kimin Lee:
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models. CoRR abs/2305.16381 (2023) - [i298]Hao Liu, Pieter Abbeel:
Emergent Agentic Transformer from Chain of Hindsight Experience. CoRR abs/2305.16554 (2023) - [i297]Hao Liu, Pieter Abbeel:
Blockwise Parallel Transformer for Long Context Large Models. CoRR abs/2305.19370 (2023) - [i296]Dongyoung Kim, Jinwoo Shin, Pieter Abbeel, Younggyo Seo:
Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration. CoRR abs/2305.19476 (2023) - [i295]Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama, Aravind Rajeswaran, Jyothish Pari, Kyle Hatch, Aryan Jain, Tianhe Yu, Pieter Abbeel, Lerrel Pinto, Chelsea Finn, Abhinav Gupta:
Train Offline, Test Online: A Real Robot Learning Benchmark. CoRR abs/2306.00942 (2023) - [i294]Mengjiao Yang, Yilun Du, Bo Dai, Dale Schuurmans, Joshua B. Tenenbaum, Pieter Abbeel:
Probabilistic Adaptation of Text-to-Video Models. CoRR abs/2306.01872 (2023) - [i293]Xinran Liang, Anthony Han, Wilson Yan, Aditi Raghunathan, Pieter Abbeel:
ALP: Action-Aware Embodied Learning for Perception. CoRR abs/2306.10190 (2023) - [i292]Joey Hejna, Pieter Abbeel, Lerrel Pinto:
Improving Long-Horizon Imitation Through Instruction Prediction. CoRR abs/2306.12554 (2023) - [i291]Xingyu Lin, John So, Sashwat Mahalingam, Fangchen Liu, Pieter Abbeel:
SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Networks. CoRR abs/2307.03567 (2023) - [i290]Nikhil Mishra, Pieter Abbeel, Xi Chen, Maximilian Sieb:
Convolutional Occupancy Models for Dense Packing of Complex, Novel Objects. CoRR abs/2308.00091 (2023) - [i289]Jessy Lin, Yuqing Du, Olivia Watkins, Danijar Hafner, Pieter Abbeel, Dan Klein, Anca D. Dragan:
Learning to Model the World with Language. CoRR abs/2308.01399 (2023) - [i288]Hiroshi Yoshitake, Pieter Abbeel:
The Impact of Overall Optimization on Warehouse Automation. CoRR abs/2308.06036 (2023) - [i287]Ademi Adeniji, Amber Xie, Carmelo Sferrazza, Younggyo Seo, Stephen James, Pieter Abbeel:
Language Reward Modulation for Pretraining Reinforcement Learning. CoRR abs/2308.12270 (2023) - [i286]Amber Xie, Youngwoon Lee, Pieter Abbeel, Stephen James:
Language-Conditioned Path Planning. CoRR abs/2308.16893 (2023) - [i285]Philipp Wu, Yide Shentu, Zhongke Yi, Xingyu Lin, Pieter Abbeel:
GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators. CoRR abs/2309.13037 (2023) - [i284]Jiangliu Wang, Jianbo Jiao, Yibing Song, Stephen James, Zhan Tong, Chongjian Ge, Pieter Abbeel, Yunhui Liu:
Speed Co-Augmentation for Unsupervised Audio-Visual Pre-training. CoRR abs/2309.13942 (2023) - [i283]Hao Liu, Matei Zaharia, Pieter Abbeel:
Ring Attention with Blockwise Transformers for Near-Infinite Context. CoRR abs/2310.01889 (2023) - [i282]Weirui Ye, Yunsheng Zhang, Mengchen Wang, Shengjie Wang, Xianfan Gu, Pieter Abbeel, Yang Gao:
Foundation Reinforcement Learning: towards Embodied Generalist Agents with Foundation Prior Assistance. CoRR abs/2310.02635 (2023) - [i281]Mengjiao Yang, Yilun Du, Kamyar Ghasemipour, Jonathan Tompson, Dale Schuurmans, Pieter Abbeel:
Learning Interactive Real-World Simulators. CoRR abs/2310.06114 (2023) - [i280]Hao Liu, Matei Zaharia, Pieter Abbeel:
Exploration with Principles for Diverse AI Supervision. CoRR abs/2310.08899 (2023) - [i279]Yilun Du, Mengjiao Yang, Pete Florence, Fei Xia, Ayzaan Wahid, Brian Ichter, Pierre Sermanet, Tianhe Yu, Pieter Abbeel, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Andy Zeng, Jonathan Tompson:
Video Language Planning. CoRR abs/2310.10625 (2023) - [i278]Boyi Li, Philipp Wu, Pieter Abbeel, Jitendra Malik:
Interactive Task Planning with Language Models. CoRR abs/2310.10645 (2023) - [i277]Yoshua Bengio, Geoffrey E. Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian K. Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atilim Günes Baydin, Sheila A. McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca D. Dragan, Philip H. S. Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, Sören Mindermann:
Managing AI Risks in an Era of Rapid Progress. CoRR abs/2310.17688 (2023) - [i276]Carmelo Sferrazza, Younggyo Seo, Hao Liu, Youngwoon Lee, Pieter Abbeel:
The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning. CoRR abs/2311.00924 (2023) - [i275]Sam Toyer, Olivia Watkins, Ethan Adrian Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, Alan Ritter, Stuart Russell:
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game. CoRR abs/2311.01011 (2023) - [i274]Vint Lee, Pieter Abbeel, Youngwoon Lee:
DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing. CoRR abs/2311.01450 (2023) - [i273]Daiki E. Matsunaga, Jongmin Lee, Jaeseok Yoon, Stefanos Leonardos, Pieter Abbeel, Kee-Eung Kim:
AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation. CoRR abs/2311.02194 (2023) - [i272]Mengjiao Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans, Igor Mordatch, Ekin Dogus Cubuk:
Scalable Diffusion for Materials Generation. CoRR abs/2311.09235 (2023) - [i271]Wilson Yan, Andrew Brown, Pieter Abbeel, Rohit Girdhar, Samaneh Azadi:
Motion-Conditioned Image Animation for Video Editing. CoRR abs/2311.18827 (2023) - [i270]Michael Psenka, Alejandro Escontrela, Pieter Abbeel, Yi Ma:
Learning a Diffusion Model Policy from Rewards via Q-Score Matching. CoRR abs/2312.11752 (2023) - 2022
- [i269]Wenlong Huang, Pieter Abbeel, Deepak Pathak, Igor Mordatch:
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents. CoRR abs/2201.07207 (2022) - [i268]Julius Frost, Olivia Watkins, Eric Weiner, Pieter Abbeel, Trevor Darrell, Bryan A. Plummer, Kate Saenko:
Explaining Reinforcement Learning Policies through Counterfactual Trajectories. CoRR abs/2201.12462 (2022) - [i267]Denis Yarats, David Brandfonbrener, Hao Liu, Michael Laskin, Pieter Abbeel, Alessandro Lazaric, Lerrel Pinto:
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning. CoRR abs/2201.13425 (2022) - [i266]Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel:
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery. CoRR abs/2202.00161 (2022) - [i265]Stephen James, Pieter Abbeel:
Bingham Policy Parameterization for 3D Rotations in Reinforcement Learning. CoRR abs/2202.03957 (2022) - [i264]Yuqing Du, Pieter Abbeel, Aditya Grover:
It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum Generation. CoRR abs/2202.10608 (2022) - [i263]Jongjin Park, Younggyo Seo, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee:
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning. CoRR abs/2203.10050 (2022) - [i262]Olivia Watkins, Trevor Darrell, Pieter Abbeel, Jacob Andreas, Abhishek Gupta:
Teachable Reinforcement Learning via Advice Distillation. CoRR abs/2203.11197 (2022) - [i261]Younggyo Seo, Kimin Lee, Stephen James, Pieter Abbeel:
Reinforcement Learning with Action-Free Pre-Training from Videos. CoRR abs/2203.13880 (2022) - [i260]Alejandro Escontrela, Xue Bin Peng, Wenhao Yu, Tingnan Zhang, Atil Iscen, Ken Goldberg, Pieter Abbeel:
Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions. CoRR abs/2203.15103 (2022) - [i259]Kourosh Hakhamaneshi, Marcel Nassar, Mariano Phielipp, Pieter Abbeel, Vladimir Stojanovic:
Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design. CoRR abs/2203.15913 (2022) - [i258]Stephen James, Pieter Abbeel:
Coarse-to-Fine Q-attention with Learned Path Ranking. CoRR abs/2204.01571 (2022) - [i257]Carl Qi, Pieter Abbeel, Aditya Grover:
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning. CoRR abs/2204.03597 (2022) - [i256]Kai Chen, Rui Cao, Stephen James, Yichuan Li, Yun-Hui Liu, Pieter Abbeel, Qi Dou:
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin-picking. CoRR abs/2204.07049 (2022) - [i255]Stephen James, Pieter Abbeel:
Coarse-to-fine Q-attention with Tree Expansion. CoRR abs/2204.12471 (2022) - [i254]Xin Chen, Sam Toyer, Cody Wild, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven H. Wang, Ping Luo, Stuart Russell, Pieter Abbeel, Rohin Shah:
An Empirical Investigation of Representation Learning for Imitation. CoRR abs/2205.07886 (2022) - [i253]Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Chain of Thought Imitation with Procedure Cloning. CoRR abs/2205.10816 (2022) - [i252]Xinran Liang, Katherine Shu, Kimin Lee, Pieter Abbeel:
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning. CoRR abs/2205.12401 (2022) - [i251]Xinyang Geng, Hao Liu, Lisa Lee, Dale Schuurams, Sergey Levine, Pieter Abbeel:
Multimodal Masked Autoencoders Learn Transferable Representations. CoRR abs/2205.14204 (2022) - [i250]Mandi Zhao, Pieter Abbeel, Stephen James:
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning. CoRR abs/2206.03271 (2022) - [i249]Wilson Yan, Ryo Okumura, Stephen James, Pieter Abbeel:
Patch-based Object-centric Transformers for Efficient Video Generation. CoRR abs/2206.04003 (2022) - [i248]Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel:
Deep Hierarchical Planning from Pixels. CoRR abs/2206.04114 (2022) - [i247]Philipp Wu, Alejandro Escontrela, Danijar Hafner, Ken Goldberg, Pieter Abbeel:
DayDreamer: World Models for Physical Robot Learning. CoRR abs/2206.14176 (2022) - [i246]Younggyo Seo, Danijar Hafner, Hao Liu, Fangchen Liu, Stephen James, Kimin Lee, Pieter Abbeel:
Masked World Models for Visual Control. CoRR abs/2206.14244 (2022) - [i245]Ryan Hoque, Lawrence Yunliang Chen, Satvik Sharma, Karthik Dharmarajan, Brijen Thananjeyan, Pieter Abbeel, Ken Goldberg:
Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision. CoRR abs/2206.14349 (2022) - [i244]Qiyang Li, Ajay Jain, Pieter Abbeel:
AdaCat: Adaptive Categorical Discretization for Autoregressive Models. CoRR abs/2208.02246 (2022) - [i243]Kyle Hollins Wray, Stas Tiomkin, Mykel J. Kochenderfer, Pieter Abbeel:
Multi-Objective Policy Gradients with Topological Constraints. CoRR abs/2209.07096 (2022) - [i242]Younggyo Seo, Kimin Lee, Fangchen Liu, Stephen James, Pieter Abbeel:
HARP: Autoregressive Latent Video Prediction with High-Fidelity Image Generator. CoRR abs/2209.07143 (2022) - [i241]Litian Liang, Yaosheng Xu, Stephen McAleer, Dailin Hu, Alexander Ihler, Pieter Abbeel, Roy Fox:
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks. CoRR abs/2209.07670 (2022) - [i240]Wilson Yan, Danijar Hafner, Stephen James, Pieter Abbeel:
Temporally Consistent Video Transformer for Long-Term Video Prediction. CoRR abs/2210.02396 (2022) - [i239]Ilija Radosavovic, Tete Xiao, Stephen James, Pieter Abbeel, Jitendra Malik, Trevor Darrell:
Real-World Robot Learning with Masked Visual Pre-training. CoRR abs/2210.03109 (2022) - [i238]Yuxuan Liu, Nikhil Mishra, Maximilian Sieb, Yide Shentu, Pieter Abbeel, Xi Chen:
Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction. CoRR abs/2210.07424 (2022) - [i237]Ademi Adeniji, Amber Xie, Pieter Abbeel:
Skill-Based Reinforcement Learning with Intrinsic Reward Matching. CoRR abs/2210.07426 (2022) - [i236]Abdus Salam Azad, Izzeddin Gur, Aleksandra Faust, Pieter Abbeel, Ion Stoica:
CLUTR: Curriculum Learning via Unsupervised Task Representation Learning. CoRR abs/2210.10243 (2022) - [i235]Weirui Ye, Pieter Abbeel, Yang Gao:
Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions. CoRR abs/2210.12628 (2022) - [i234]Hao Liu, Lisa Lee, Kimin Lee, Pieter Abbeel:
Instruction-Following Agents with Jointly Pre-Trained Vision-Language Models. CoRR abs/2210.13431 (2022) - [i233]Hao Liu, Xinyang Geng, Lisa Lee, Igor Mordatch, Sergey Levine, Sharan Narang, Pieter Abbeel:
FCM: Forgetful Causal Masking Makes Causal Language Models Better Zero-Shot Learners. CoRR abs/2210.13432 (2022) - [i232]Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Dichotomy of Control: Separating What You Can Control from What You Cannot. CoRR abs/2210.13435 (2022) - [i231]John So, Amber Xie, Sunggoo Jung, Jeffrey A. Edlund, Rohan Thakker, Ali-Akbar Agha-Mohammadi, Pieter Abbeel, Stephen James:
Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data. CoRR abs/2210.14721 (2022) - [i230]Kai Chen, Stephen James, Congying Sui, Yun-Hui Liu, Pieter Abbeel, Qi Dou:
StereoPose: Category-Level 6D Transparent Object Pose Estimation from Stereo Images via Back-View NOCS. CoRR abs/2211.01644 (2022) - [i229]Ajay Jain, Amber Xie, Pieter Abbeel:
VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models. CoRR abs/2211.11319 (2022) - [i228]Fangchen Liu, Hao Liu, Aditya Grover, Pieter Abbeel:
Masked Autoencoding for Scalable and Generalizable Decision Making. CoRR abs/2211.12740 (2022) - [i227]David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, Ofir Nachum:
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets. CoRR abs/2211.13337 (2022) - 2021
- [i226]Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Michael Laskin:
Reinforcement Learning with Latent Flow. CoRR abs/2101.01857 (2021) - [i225]Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani:
Bottleneck Transformers for Visual Recognition. CoRR abs/2101.11605 (2021) - [i224]Younggyo Seo, Lili Chen, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee:
State Entropy Maximization with Random Encoders for Efficient Exploration. CoRR abs/2102.09430 (2021) - [i223]Donald J. Hejna III, Pieter Abbeel, Lerrel Pinto:
Task-Agnostic Morphology Evolution. CoRR abs/2102.13100 (2021) - [i222]Lili Chen, Kimin Lee, Aravind Srinivas, Pieter Abbeel:
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings. CoRR abs/2103.02886 (2021) - [i221]Hao Liu, Pieter Abbeel:
Behavior From the Void: Unsupervised Active Pre-Training. CoRR abs/2103.04551 (2021) - [i220]Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch:
Pretrained Transformers as Universal Computation Engines. CoRR abs/2103.05247 (2021) - [i219]Rui Zhao, Yang Gao, Pieter Abbeel, Volker Tresp, Wei Xu:
Mutual Information State Intrinsic Control. CoRR abs/2103.08107 (2021) - [i218]Zhongyu Li, Xuxin Cheng, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath:
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots. CoRR abs/2103.14295 (2021) - [i217]Ajay Jain, Matthew Tancik, Pieter Abbeel:
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis. CoRR abs/2104.00677 (2021) - [i216]Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, Angjoo Kanazawa:
AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control. CoRR abs/2104.02180 (2021) - [i215]Philippe Hansen-Estruch, Wenling Shang, Lerrel Pinto, Pieter Abbeel, Stas Tiomkin:
GEM: Group Enhanced Model for Learning Dynamical Control Systems. CoRR abs/2104.02844 (2021) - [i214]David Lindner, Rohin Shah, Pieter Abbeel, Anca D. Dragan:
Learning What To Do by Simulating the Past. CoRR abs/2104.03946 (2021) - [i213]Yuqing Du, Olivia Watkins, Trevor Darrell, Pieter Abbeel, Deepak Pathak:
Auto-Tuned Sim-to-Real Transfer. CoRR abs/2104.07662 (2021) - [i212]Wilson Yan, Yunzhi Zhang, Pieter Abbeel, Aravind Srinivas:
VideoGPT: Video Generation using VQ-VAE and Transformers. CoRR abs/2104.10157 (2021) - [i211]Kourosh Hakhamaneshi, Pieter Abbeel, Vladimir Stojanovic, Aditya Grover:
JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data. CoRR abs/2106.00942 (2021) - [i210]Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch:
Decision Transformer: Reinforcement Learning via Sequence Modeling. CoRR abs/2106.01345 (2021) - [i209]Kimin Lee, Laura M. Smith, Pieter Abbeel:
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training. CoRR abs/2106.05091 (2021) - [i208]Boyuan Chen, Pieter Abbeel, Deepak Pathak:
Unsupervised Learning of Visual 3D Keypoints for Control. CoRR abs/2106.07643 (2021) - [i207]Catherine Cang, Aravind Rajeswaran, Pieter Abbeel, Michael Laskin:
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL. CoRR abs/2106.09119 (2021) - [i206]Abdus Salam Azad, Edward Kim, Qiancheng Wu, Kimin Lee, Ion Stoica, Pieter Abbeel, Sanjit A. Seshia:
Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments. CoRR abs/2106.10365 (2021) - [i205]Seunghyun Lee, Younggyo Seo, Kimin Lee, Pieter Abbeel, Jinwoo Shin:
Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble. CoRR abs/2107.00591 (2021) - [i204]Rohin Shah, Cody Wild, Steven H. Wang, Neel Alex, Brandon Houghton, William H. Guss, Sharada P. Mohanty, Anssi Kanervisto, Stephanie Milani, Nicholay Topin, Pieter Abbeel, Stuart Russell, Anca D. Dragan:
The MineRL BASALT Competition on Learning from Human Feedback. CoRR abs/2107.01969 (2021) - [i203]Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan, Pieter Abbeel, Michael Laskin:
Hierarchical Few-Shot Imitation with Skill Transition Models. CoRR abs/2107.08981 (2021) - [i202]Sarah Young, Jyothish Pari, Pieter Abbeel, Lerrel Pinto:
Playful Interactions for Representation Learning. CoRR abs/2107.09046 (2021) - [i201]Xiaofei Wang, Kimin Lee, Kourosh Hakhamaneshi, Pieter Abbeel, Michael Laskin:
Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback. CoRR abs/2108.05382 (2021) - [i200]Hao Liu, Pieter Abbeel:
APS: Active Pretraining with Successor Features. CoRR abs/2108.13956 (2021) - [i199]Mandi Zhao, Fangchen Liu, Kimin Lee, Pieter Abbeel:
Towards More Generalizable One-shot Visual Imitation Learning. CoRR abs/2110.13423 (2021) - [i198]Litian Liang, Yaosheng Xu, Stephen McAleer, Dailin Hu, Alexander Ihler, Pieter Abbeel, Roy Fox:
Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates. CoRR abs/2110.14818 (2021) - [i197]Michael Laskin, Denis Yarats, Hao Liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, Pieter Abbeel:
URLB: Unsupervised Reinforcement Learning Benchmark. CoRR abs/2110.15191 (2021) - [i196]Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, Yang Gao:
Mastering Atari Games with Limited Data. CoRR abs/2111.00210 (2021) - [i195]Kimin Lee, Laura M. Smith, Anca D. Dragan, Pieter Abbeel:
B-Pref: Benchmarking Preference-Based Reinforcement Learning. CoRR abs/2111.03026 (2021) - [i194]Wenlong Huang, Igor Mordatch, Pieter Abbeel, Deepak Pathak:
Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning. CoRR abs/2111.03062 (2021) - [i193]Dailin Hu, Pieter Abbeel, Roy Fox:
Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning. CoRR abs/2111.14204 (2021) - [i192]Charles Packer, Pieter Abbeel, Joseph E. Gonzalez:
Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL. CoRR abs/2112.00901 (2021) - [i191]Ajay Jain, Ben Mildenhall, Jonathan T. Barron, Pieter Abbeel, Ben Poole:
Zero-Shot Text-Guided Object Generation with Dream Fields. CoRR abs/2112.01455 (2021) - [i190]Yaosheng Xu, Dailin Hu, Litian Liang, Stephen McAleer, Pieter Abbeel, Roy Fox:
Target Entropy Annealing for Discrete Soft Actor-Critic. CoRR abs/2112.02852 (2021) - 2020
- [i189]Albert Zhan, Stas Tiomkin, Pieter Abbeel:
Preventing Imitation Learning with Adversarial Policy Ensembles. CoRR abs/2002.01059 (2020) - [i188]Gregory Kahn, Pieter Abbeel, Sergey Levine:
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. CoRR abs/2002.05700 (2020) - [i187]Kourosh Hakhamaneshi, Keertana Settaluri, Pieter Abbeel, Vladimir Stojanovic:
GACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfaction. CoRR abs/2002.07236 (2020) - [i186]Alexander C. Li, Lerrel Pinto, Pieter Abbeel:
Generalized Hindsight for Reinforcement Learning. CoRR abs/2002.11708 (2020) - [i185]Kara Liu, Thanard Kurutach, Christine Tung, Pieter Abbeel, Aviv Tamar:
Hallucinative Topological Memory for Zero-Shot Visual Planning. CoRR abs/2002.12336 (2020) - [i184]Donald J. Hejna III, Pieter Abbeel, Lerrel Pinto:
Hierarchically Decoupled Imitation for Morphological Transfer. CoRR abs/2003.01709 (2020) - [i183]Wilson Yan, Ashwin Vangipuram, Pieter Abbeel, Lerrel Pinto:
Learning Predictive Representations for Deformable Objects Using Contrastive Estimation. CoRR abs/2003.05436 (2020) - [i182]Michael Laskin, Scott Emmons, Ajay Jain, Thanard Kurutach, Pieter Abbeel, Deepak Pathak:
Sparse Graphical Memory for Robust Planning. CoRR abs/2003.06417 (2020) - [i181]Aravind Srinivas, Michael Laskin, Pieter Abbeel:
CURL: Contrastive Unsupervised Representations for Reinforcement Learning. CoRR abs/2004.04136 (2020) - [i180]Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas:
Reinforcement Learning with Augmented Data. CoRR abs/2004.14990 (2020) - [i179]Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra:
Plan2Vec: Unsupervised Representation Learning by Latent Plans. CoRR abs/2005.03648 (2020) - [i178]Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak:
Planning to Explore via Self-Supervised World Models. CoRR abs/2005.05960 (2020) - [i177]Ignasi Clavera, Violet Fu, Pieter Abbeel:
Model-Augmented Actor-Critic: Backpropagating through Paths. CoRR abs/2005.08068 (2020) - [i176]Yiming Ding, Ignasi Clavera, Pieter Abbeel:
Mutual Information Maximization for Robust Plannable Representations. CoRR abs/2005.08114 (2020) - [i175]Yunzhi Zhang, Pieter Abbeel, Lerrel Pinto:
Automatic Curriculum Learning through Value Disagreement. CoRR abs/2006.09641 (2020) - [i174]Jonathan Ho, Ajay Jain, Pieter Abbeel:
Denoising Diffusion Probabilistic Models. CoRR abs/2006.11239 (2020) - [i173]Ajay Jain, Pieter Abbeel, Deepak Pathak:
Locally Masked Convolution for Autoregressive Models. CoRR abs/2006.12486 (2020) - [i172]Yuqing Du, Stas Tiomkin, Emre Kiciman, Daniel Polani, Pieter Abbeel, Anca D. Dragan:
AvE: Assistance via Empowerment. CoRR abs/2006.14796 (2020) - [i171]Adam Stooke, Joshua Achiam, Pieter Abbeel:
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods. CoRR abs/2007.03964 (2020) - [i170]Nicklas Hansen, Yu Sun, Pieter Abbeel, Alexei A. Efros, Lerrel Pinto, Xiaolong Wang:
Self-Supervised Policy Adaptation during Deployment. CoRR abs/2007.04309 (2020) - [i169]Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel:
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning. CoRR abs/2007.04938 (2020) - [i168]Paras Jain, Ajay Jain, Tianjun Zhang, Pieter Abbeel, Joseph E. Gonzalez, Ion Stoica:
Contrastive Code Representation Learning. CoRR abs/2007.04973 (2020) - [i167]Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan, Xi Chen:
Variable Skipping for Autoregressive Range Density Estimation. CoRR abs/2007.05572 (2020) - [i166]Ruihan Zhao, Pieter Abbeel, Stas Tiomkin:
Efficient Online Estimation of Empowerment for Reinforcement Learning. CoRR abs/2007.07356 (2020) - [i165]Hao Liu, Pieter Abbeel:
Hybrid Discriminative-Generative Training via Contrastive Learning. CoRR abs/2007.09070 (2020) - [i164]Xingyu Lu, Kimin Lee, Pieter Abbeel, Stas Tiomkin:
Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning. CoRR abs/2008.00614 (2020) - [i163]Eugene Vinitsky, Yuqing Du, Kanaad Parvate, Kathy Jang, Pieter Abbeel, Alexandre M. Bayen:
Robust Reinforcement Learning using Adversarial Populations. CoRR abs/2008.01825 (2020) - [i162]Sarah Young, Dhiraj Gandhi, Shubham Tulsiani, Abhinav Gupta, Pieter Abbeel, Lerrel Pinto:
Visual Imitation Made Easy. CoRR abs/2008.04899 (2020) - [i161]Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin:
Decoupling Representation Learning from Reinforcement Learning. CoRR abs/2009.08319 (2020) - [i160]Gregory Kahn, Pieter Abbeel, Sergey Levine:
LaND: Learning to Navigate from Disengagements. CoRR abs/2010.04689 (2020) - [i159]Younggyo Seo, Kimin Lee, Ignasi Clavera, Thanard Kurutach, Jinwoo Shin, Pieter Abbeel:
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning. CoRR abs/2010.13303 (2020) - [i158]Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch:
Reset-Free Lifelong Learning with Skill-Space Planning. CoRR abs/2012.03548 (2020) - [i157]Michael Laskin, Luke Metz, Seth Nabarrao, Mark Saroufim, Badreddine Noune, Carlo Luschi, Jascha Sohl-Dickstein, Pieter Abbeel:
Parallel Training of Deep Networks with Local Updates. CoRR abs/2012.03837 (2020) - [i156]Albert Zhan, Philip Zhao, Lerrel Pinto, Pieter Abbeel, Michael Laskin:
A Framework for Efficient Robotic Manipulation. CoRR abs/2012.07975 (2020) - 2019
- [i155]Himanshu Sahni, Toby Buckley, Pieter Abbeel, Ilya Kuzovkin:
Visual Hindsight Experience Replay. CoRR abs/1901.11529 (2019) - [i154]Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel:
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design. CoRR abs/1902.00275 (2019) - [i153]Katie Kang, Suneel Belkhale, Gregory Kahn, Pieter Abbeel, Sergey Levine:
Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight. CoRR abs/1902.03701 (2019) - [i152]Rohin Shah, Dmitrii Krasheninnikov, Jordan Alexander, Pieter Abbeel, Anca D. Dragan:
Preferences Implicit in the State of the World. CoRR abs/1902.04198 (2019) - [i151]Jianlan Luo, Eugen Solowjow, Chengtao Wen, Juan Aparicio Ojea, Alice M. Agogino, Aviv Tamar, Pieter Abbeel:
Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly. CoRR abs/1903.01066 (2019) - [i150]Xinyi Ren, Jianlan Luo, Eugen Solowjow, Juan Aparicio Ojea, Abhishek Gupta, Aviv Tamar, Pieter Abbeel:
Domain Randomization for Active Pose Estimation. CoRR abs/1903.03953 (2019) - [i149]Joshua Achiam, Ethan Knight, Pieter Abbeel:
Towards Characterizing Divergence in Deep Q-Learning. CoRR abs/1903.08894 (2019) - [i148]Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn:
Guided Meta-Policy Search. CoRR abs/1904.00956 (2019) - [i147]David V. Gealy, Stephen McKinley, Brent Yi, Philipp Wu, Phillip R. Downey, Greg Balke, Allan Zhao, Menglong Guo, Rachel Thomasson, Anthony Sinclair, Peter Cuellar, Zoe McCarthy, Pieter Abbeel:
Quasi-Direct Drive for Low-Cost Compliant Robotic Manipulation. CoRR abs/1904.03815 (2019) - [i146]Zongheng Yang, Eric Liang, Amog Kamsetty, Chenggang Wu, Yan Duan, Xi Chen, Pieter Abbeel, Joseph M. Hellerstein, Sanjay Krishnan, Ion Stoica:
Selectivity Estimation with Deep Likelihood Models. CoRR abs/1905.04278 (2019) - [i145]Angelina Wang, Thanard Kurutach, Kara Liu, Pieter Abbeel, Aviv Tamar:
Learning Robotic Manipulation through Visual Planning and Acting. CoRR abs/1905.04411 (2019) - [i144]Daniel Ho, Eric Liang, Ion Stoica, Pieter Abbeel, Xi Chen:
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules. CoRR abs/1905.05393 (2019) - [i143]Friso H. Kingma, Pieter Abbeel, Jonathan Ho:
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables. CoRR abs/1905.06845 (2019) - [i142]Jonathan Ho, Evan Lohn, Pieter Abbeel:
Compression with Flows via Local Bits-Back Coding. CoRR abs/1905.08500 (2019) - [i141]Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine:
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies. CoRR abs/1905.09808 (2019) - [i140]Giulia Vezzani, Abhishek Gupta, Lorenzo Natale, Pieter Abbeel:
Learning latent state representation for speeding up exploration. CoRR abs/1905.12621 (2019) - [i139]Yiming Ding, Carlos Florensa, Mariano Phielipp, Pieter Abbeel:
Goal-conditioned Imitation Learning. CoRR abs/1906.05838 (2019) - [i138]Alexander C. Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel:
Sub-policy Adaptation for Hierarchical Reinforcement Learning. CoRR abs/1906.05862 (2019) - [i137]Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John F. Canny, Pieter Abbeel, Yun S. Song:
Evaluating Protein Transfer Learning with TAPE. CoRR abs/1906.08230 (2019) - [i136]Rohin Shah, Noah Gundotra, Pieter Abbeel, Anca D. Dragan:
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference. CoRR abs/1906.09624 (2019) - [i135]Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine:
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model. CoRR abs/1907.00953 (2019) - [i134]Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric D. Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, Jimmy Ba:
Benchmarking Model-Based Reinforcement Learning. CoRR abs/1907.02057 (2019) - [i133]Kourosh Hakhamaneshi, Nick Werblun, Pieter Abbeel, Vladimir Stojanovic:
BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks. CoRR abs/1907.10515 (2019) - [i132]Hari Prasanna Das, Pieter Abbeel, Costas J. Spanos:
Dimensionality Reduction Flows. CoRR abs/1908.01686 (2019) - [i131]Yusuke Urakami, Alec Hodgkinson, Casey Carlin, Randall Leu, Luca Rigazio, Pieter Abbeel:
DoorGym: A Scalable Door Opening Environment And Baseline Agent. CoRR abs/1908.01887 (2019) - [i130]Adam Stooke, Pieter Abbeel:
rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch. CoRR abs/1909.01500 (2019) - [i129]Paras Jain, Ajay Jain, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Kurt Keutzer, Ion Stoica, Joseph E. Gonzalez:
Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization. CoRR abs/1910.02653 (2019) - [i128]Micah Carroll, Rohin Shah, Mark K. Ho, Thomas L. Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca D. Dragan:
On the Utility of Learning about Humans for Human-AI Coordination. CoRR abs/1910.05789 (2019) - [i127]Yunzhi Zhang, Ignasi Clavera, Boren Tsai, Pieter Abbeel:
Asynchronous Methods for Model-Based Reinforcement Learning. CoRR abs/1910.12453 (2019) - [i126]Yilin Wu, Wilson Yan, Thanard Kurutach, Lerrel Pinto, Pieter Abbeel:
Learning to Manipulate Deformable Objects without Demonstrations. CoRR abs/1910.13439 (2019) - [i125]Coline Devin, Daniel Geng, Pieter Abbeel, Trevor Darrell, Sergey Levine:
Plan Arithmetic: Compositional Plan Vectors for Multi-Task Control. CoRR abs/1910.14033 (2019) - [i124]Josh Tobin, OpenAI Robotics, Pieter Abbeel:
Geometry-Aware Neural Rendering. CoRR abs/1911.04554 (2019) - [i123]Kevin Lu, Igor Mordatch, Pieter Abbeel:
Adaptive Online Planning for Continual Lifelong Learning. CoRR abs/1912.01188 (2019) - [i122]Ruihan Zhao, Stas Tiomkin, Pieter Abbeel:
Learning Efficient Representation for Intrinsic Motivation. CoRR abs/1912.02624 (2019) - [i121]Laura M. Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, Sergey Levine:
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos. CoRR abs/1912.04443 (2019) - [i120]Wilson Yan, Jonathan Ho, Pieter Abbeel:
Natural Image Manipulation for Autoregressive Models Using Fisher Scores. CoRR abs/1912.05015 (2019) - [i119]Roy Fox, Richard Shin, William Paul, Yitian Zou, Dawn Song, Ken Goldberg, Pieter Abbeel, Ion Stoica:
Hierarchical Variational Imitation Learning of Control Programs. CoRR abs/1912.12612 (2019) - [i118]Xingyu Lu, Stas Tiomkin, Pieter Abbeel:
Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards. CoRR abs/1912.13414 (2019) - 2018
- [i117]Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine:
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. CoRR abs/1801.01290 (2018) - [i116]Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. CoRR abs/1802.01557 (2018) - [i115]Rein Houthooft, Richard Y. Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, Pieter Abbeel:
Evolved Policy Gradients. CoRR abs/1802.04821 (2018) - [i114]Abhishek Gupta, Russell Mendonca, Yuxuan Liu, Pieter Abbeel, Sergey Levine:
Meta-Reinforcement Learning of Structured Exploration Strategies. CoRR abs/1802.07245 (2018) - [i113]Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel:
Model-Ensemble Trust-Region Policy Optimization. CoRR abs/1802.10592 (2018) - [i112]Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever:
Some Considerations on Learning to Explore via Meta-Reinforcement Learning. CoRR abs/1803.01118 (2018) - [i111]Adam Stooke, Pieter Abbeel:
Accelerated Methods for Deep Reinforcement Learning. CoRR abs/1803.02811 (2018) - [i110]Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine:
Composable Deep Reinforcement Learning for Robotic Manipulation. CoRR abs/1803.06773 (2018) - [i109]Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham M. Kakade, Igor Mordatch, Pieter Abbeel:
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines. CoRR abs/1803.07246 (2018) - [i108]Garrett Thomas, Melissa Chien, Aviv Tamar, Juan Aparicio Ojea, Pieter Abbeel:
Learning Robotic Assembly from CAD. CoRR abs/1803.07635 (2018) - [i107]Ignasi Clavera, Anusha Nagabandi, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn:
Learning to Adapt: Meta-Learning for Model-Based Control. CoRR abs/1803.11347 (2018) - [i106]Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn:
Universal Planning Networks. CoRR abs/1804.00645 (2018) - [i105]Alex X. Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, Sergey Levine:
Stochastic Adversarial Video Prediction. CoRR abs/1804.01523 (2018) - [i104]Xue Bin Peng, Pieter Abbeel, Sergey Levine, Michiel van de Panne:
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills. CoRR abs/1804.02717 (2018) - [i103]Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, Sergey Levine:
Latent Space Policies for Hierarchical Reinforcement Learning. CoRR abs/1804.02808 (2018) - [i102]Niko Sünderhauf, Oliver Brock, Walter J. Scheirer, Raia Hadsell, Dieter Fox, Jürgen Leitner, Ben Upcroft, Pieter Abbeel, Wolfram Burgard, Michael Milford, Peter Corke:
The Limits and Potentials of Deep Learning for Robotics. CoRR abs/1804.06557 (2018) - [i101]John D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine:
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings. CoRR abs/1806.02813 (2018) - [i100]Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart Russell, Pieter Abbeel:
Learning Plannable Representations with Causal InfoGAN. CoRR abs/1807.09341 (2018) - [i99]Joshua Achiam, Harrison Edwards, Dario Amodei, Pieter Abbeel:
Variational Option Discovery Algorithms. CoRR abs/1807.10299 (2018) - [i98]Sören R. Künzel, Bradly C. Stadie, Nikita Vemuri, Varsha Ramakrishnan, Jasjeet S. Sekhon, Pieter Abbeel:
Transfer Learning for Estimating Causal Effects using Neural Networks. CoRR abs/1808.07804 (2018) - [i97]Marvin Zhang, Sharad Vikram, Laura M. Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine:
SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning. CoRR abs/1808.09105 (2018) - [i96]Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel:
Model-Based Reinforcement Learning via Meta-Policy Optimization. CoRR abs/1809.05214 (2018) - [i95]Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, Sergey Levine:
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow. CoRR abs/1810.00821 (2018) - [i94]Xue Bin Peng, Angjoo Kanazawa, Jitendra Malik, Pieter Abbeel, Sergey Levine:
SFV: Reinforcement Learning of Physical Skills from Videos. CoRR abs/1810.03599 (2018) - [i93]Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, Pieter Abbeel:
ProMP: Proximal Meta-Policy Search. CoRR abs/1810.06784 (2018) - [i92]Gregory Kahn, Adam Villaflor, Pieter Abbeel, Sergey Levine:
Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation. CoRR abs/1810.07167 (2018) - [i91]Sandy H. Huang, Kush Bhatia, Pieter Abbeel, Anca D. Dragan:
Establishing Appropriate Trust via Critical States. CoRR abs/1810.08174 (2018) - [i90]Tianhe Yu, Pieter Abbeel, Sergey Levine, Chelsea Finn:
One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks. CoRR abs/1810.11043 (2018) - [i89]Dennis Lee, Haoran Tang, Jeffrey O. Zhang, Huazhe Xu, Trevor Darrell, Pieter Abbeel:
Modular Architecture for StarCraft II with Deep Reinforcement Learning. CoRR abs/1811.03555 (2018) - [i88]Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, Jan Peters:
An Algorithmic Perspective on Imitation Learning. CoRR abs/1811.06711 (2018) - [i87]John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev, Nick Altieri, John DeNero, Pieter Abbeel, Sergey Levine:
Guiding Policies with Language via Meta-Learning. CoRR abs/1811.07882 (2018) - [i86]Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, Sergey Levine:
Soft Actor-Critic Algorithms and Applications. CoRR abs/1812.05905 (2018) - 2017
- [i85]Nithyanand Kota, Abhishek Mishra, Sunil Srinivasa, Xi Chen, Pieter Abbeel:
A K-fold Method for Baseline Estimation in Policy Gradient Algorithms. CoRR abs/1701.00867 (2017) - [i84]Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, Sergey Levine:
Uncertainty-Aware Reinforcement Learning for Collision Avoidance. CoRR abs/1702.01182 (2017) - [i83]Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel:
Adversarial Attacks on Neural Network Policies. CoRR abs/1702.02284 (2017) - [i82]Sandy H. Huang, David Held, Pieter Abbeel, Anca D. Dragan:
Enabling Robots to Communicate their Objectives. CoRR abs/1702.03465 (2017) - [i81]Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine:
Reinforcement Learning with Deep Energy-Based Policies. CoRR abs/1702.08165 (2017) - [i80]Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever:
Third-Person Imitation Learning. CoRR abs/1703.01703 (2017) - [i79]Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, Sergey Levine:
Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation. CoRR abs/1703.02018 (2017) - [i78]Abhishek Gupta, Coline Devin, Yuxuan Liu, Pieter Abbeel, Sergey Levine:
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning. CoRR abs/1703.02949 (2017) - [i77]Chelsea Finn, Pieter Abbeel, Sergey Levine:
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. CoRR abs/1703.03400 (2017) - [i76]Nikhil Mishra, Pieter Abbeel, Igor Mordatch:
Prediction and Control with Temporal Segment Models. CoRR abs/1703.04070 (2017) - [i75]Igor Mordatch, Pieter Abbeel:
Emergence of Grounded Compositional Language in Multi-Agent Populations. CoRR abs/1703.04908 (2017) - [i74]Joshua Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, Pieter Abbeel:
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World. CoRR abs/1703.06907 (2017) - [i73]Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba:
One-Shot Imitation Learning. CoRR abs/1703.07326 (2017) - [i72]Alex X. Lee, Sergey Levine, Pieter Abbeel:
Learning Visual Servoing with Deep Features and Fitted Q-Iteration. CoRR abs/1703.11000 (2017) - [i71]Carlos Florensa, Yan Duan, Pieter Abbeel:
Stochastic Neural Networks for Hierarchical Reinforcement Learning. CoRR abs/1704.03012 (2017) - [i70]John Schulman, Pieter Abbeel, Xi Chen:
Equivalence Between Policy Gradients and Soft Q-Learning. CoRR abs/1704.06440 (2017) - [i69]David Held, Zoe McCarthy, Michael Zhang, Fred Shentu, Pieter Abbeel:
Probabilistically Safe Policy Transfer. CoRR abs/1705.05394 (2017) - [i68]David Held, Xinyang Geng, Carlos Florensa, Pieter Abbeel:
Automatic Goal Generation for Reinforcement Learning Agents. CoRR abs/1705.06366 (2017) - [i67]Joshua Achiam, David Held, Aviv Tamar, Pieter Abbeel:
Constrained Policy Optimization. CoRR abs/1705.10528 (2017) - [i66]Richard Y. Chen, Szymon Sidor, Pieter Abbeel, John Schulman:
UCB and InfoGain Exploration via $\boldsymbol{Q}$-Ensembles. CoRR abs/1706.01502 (2017) - [i65]Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y. Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, Marcin Andrychowicz:
Parameter Space Noise for Exploration. CoRR abs/1706.01905 (2017) - [i64]Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, Igor Mordatch:
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. CoRR abs/1706.02275 (2017) - [i63]Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, Wojciech Zaremba:
Hindsight Experience Replay. CoRR abs/1707.01495 (2017) - [i62]Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel:
Meta-Learning with Temporal Convolutions. CoRR abs/1707.03141 (2017) - [i61]Yuxuan Liu, Abhishek Gupta, Pieter Abbeel, Sergey Levine:
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation. CoRR abs/1707.03374 (2017) - [i60]Carlos Florensa, David Held, Markus Wulfmeier, Pieter Abbeel:
Reverse Curriculum Generation for Reinforcement Learning. CoRR abs/1707.05300 (2017) - [i59]Markus Wulfmeier, Ingmar Posner, Pieter Abbeel:
Mutual Alignment Transfer Learning. CoRR abs/1707.07907 (2017) - [i58]Coline Devin, Pieter Abbeel, Trevor Darrell, Sergey Levine:
Deep Object-Centric Representations for Generalizable Robot Learning. CoRR abs/1708.04225 (2017) - [i57]Edward Groshev, Aviv Tamar, Siddharth Srivastava, Pieter Abbeel:
Learning Generalized Reactive Policies using Deep Neural Networks. CoRR abs/1708.07280 (2017) - [i56]Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, Igor Mordatch:
Learning with Opponent-Learning Awareness. CoRR abs/1709.04326 (2017) - [i55]Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Visual Imitation Learning via Meta-Learning. CoRR abs/1709.04905 (2017) - [i54]Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel:
Overcoming Exploration in Reinforcement Learning with Demonstrations. CoRR abs/1709.10089 (2017) - [i53]Gregory Kahn, Adam Villaflor, Bosen Ding, Pieter Abbeel, Sergey Levine:
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation. CoRR abs/1709.10489 (2017) - [i52]Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel:
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments. CoRR abs/1710.03641 (2017) - [i51]Adam Stooke, Pieter Abbeel:
Synkhronos: a Multi-GPU Theano Extension for Data Parallelism. CoRR abs/1710.04162 (2017) - [i50]Tianhao Zhang, Zoe McCarthy, Owen Jow, Dennis Lee, Ken Goldberg, Pieter Abbeel:
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation. CoRR abs/1710.04615 (2017) - [i49]Joshua Tobin, Wojciech Zaremba, Pieter Abbeel:
Domain Randomization and Generative Models for Robotic Grasping. CoRR abs/1710.06425 (2017) - [i48]Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel:
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization. CoRR abs/1710.06537 (2017) - [i47]Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, Pieter Abbeel:
Asymmetric Actor Critic for Image-Based Robot Learning. CoRR abs/1710.06542 (2017) - [i46]Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman:
Meta Learning Shared Hierarchies. CoRR abs/1710.09767 (2017) - [i45]Smitha Milli, Pieter Abbeel, Igor Mordatch:
Interpretable and Pedagogical Examples. CoRR abs/1711.00694 (2017) - [i44]Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart Russell, Anca D. Dragan:
Inverse Reward Design. CoRR abs/1711.02827 (2017) - [i43]William Wang, Angelina Wang, Aviv Tamar, Xi Chen, Pieter Abbeel:
Safer Classification by Synthesis. CoRR abs/1711.08534 (2017) - [i42]Ion Stoica, Dawn Song, Raluca Ada Popa, David A. Patterson, Michael W. Mahoney, Randy H. Katz, Anthony D. Joseph, Michael I. Jordan, Joseph M. Hellerstein, Joseph E. Gonzalez, Ken Goldberg, Ali Ghodsi, David E. Culler, Pieter Abbeel:
A Berkeley View of Systems Challenges for AI. CoRR abs/1712.05855 (2017) - [i41]Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel:
PixelSNAIL: An Improved Autoregressive Generative Model. CoRR abs/1712.09763 (2017) - 2016
- [i40]Aviv Tamar, Sergey Levine, Pieter Abbeel:
Value Iteration Networks. CoRR abs/1602.02867 (2016) - [i39]Chelsea Finn, Sergey Levine, Pieter Abbeel:
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization. CoRR abs/1603.00448 (2016) - [i38]Gregory Kahn, Tianhao Zhang, Sergey Levine, Pieter Abbeel:
PLATO: Policy Learning using Adaptive Trajectory Optimization. CoRR abs/1603.00622 (2016) - [i37]Abhishek Gupta, Clemens Eppner, Sergey Levine, Pieter Abbeel:
Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration. CoRR abs/1603.06348 (2016) - [i36]Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel:
Benchmarking Deep Reinforcement Learning for Continuous Control. CoRR abs/1604.06778 (2016) - [i35]Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel:
Backprop KF: Learning Discriminative Deterministic State Estimators. CoRR abs/1605.07148 (2016) - [i34]Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel:
Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks. CoRR abs/1605.09674 (2016) - [i33]Dylan Hadfield-Menell, Anca D. Dragan, Pieter Abbeel, Stuart Russell:
Cooperative Inverse Reinforcement Learning. CoRR abs/1606.03137 (2016) - [i32]Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel:
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. CoRR abs/1606.03657 (2016) - [i31]Pulkit Agrawal, Ashvin Nair, Pieter Abbeel, Jitendra Malik, Sergey Levine:
Learning to Poke by Poking: Experiential Learning of Intuitive Physics. CoRR abs/1606.07419 (2016) - [i30]Pieter Abbeel, Ken Goldberg, Gregory D. Hager, Julie Shah:
Toward a Science of Autonomy for Physical Systems: Paths. CoRR abs/1609.05814 (2016) - [i29]Coline Devin, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Sergey Levine:
Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer. CoRR abs/1609.07088 (2016) - [i28]Aviv Tamar, Garrett Thomas, Tianhao Zhang, Sergey Levine, Pieter Abbeel:
Learning from the Hindsight Plan - Episodic MPC Improvement. CoRR abs/1609.09001 (2016) - [i27]Xinyang Geng, Marvin Zhang, Jonathan Bruce, Ken Caluwaerts, Massimo Vespignani, Vytas SunSpiral, Pieter Abbeel, Sergey Levine:
Deep Reinforcement Learning for Tensegrity Robot Locomotion. CoRR abs/1609.09049 (2016) - [i26]William Montgomery, Anurag Ajay, Chelsea Finn, Pieter Abbeel, Sergey Levine:
Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States. CoRR abs/1610.01112 (2016) - [i25]Paul F. Christiano, Zain Shah, Igor Mordatch, Jonas Schneider, Trevor Blackwell, Joshua Tobin, Pieter Abbeel, Wojciech Zaremba:
Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model. CoRR abs/1610.03518 (2016) - [i24]Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel:
Variational Lossy Autoencoder. CoRR abs/1611.02731 (2016) - [i23]Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel:
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning. CoRR abs/1611.02779 (2016) - [i22]Chelsea Finn, Paul F. Christiano, Pieter Abbeel, Sergey Levine:
A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models. CoRR abs/1611.03852 (2016) - [i21]Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel:
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning. CoRR abs/1611.04717 (2016) - [i20]Dylan Hadfield-Menell, Anca D. Dragan, Pieter Abbeel, Stuart Russell:
The Off-Switch Game. CoRR abs/1611.08219 (2016) - [i19]Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine:
Generalizing Skills with Semi-Supervised Reinforcement Learning. CoRR abs/1612.00429 (2016) - 2015
- [i18]Sergey Levine, Nolan Wagener, Pieter Abbeel:
Learning Contact-Rich Manipulation Skills with Guided Policy Search. CoRR abs/1501.05611 (2015) - [i17]Berk Çalli, Aaron Walsman, Arjun Singh, Siddhartha S. Srinivasa, Pieter Abbeel, Aaron M. Dollar:
Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols. CoRR abs/1502.03143 (2015) - [i16]John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel:
Trust Region Policy Optimization. CoRR abs/1502.05477 (2015) - [i15]Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel:
End-to-End Training of Deep Visuomotor Policies. CoRR abs/1504.00702 (2015) - [i14]Jeremy Maitin-Shepard, Viren Jain, Michal Januszewski, Peter Li, Pieter Abbeel:
Combinatorial Energy Learning for Image Segmentation. CoRR abs/1506.04304 (2015) - [i13]John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel:
Gradient Estimation Using Stochastic Computation Graphs. CoRR abs/1506.05254 (2015) - [i12]Bradly C. Stadie, Sergey Levine, Pieter Abbeel:
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models. CoRR abs/1507.00814 (2015) - [i11]Marvin Zhang, Sergey Levine, Zoe McCarthy, Chelsea Finn, Pieter Abbeel:
Policy Learning with Continuous Memory States for Partially Observed Robotic Control. CoRR abs/1507.01273 (2015) - [i10]Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel:
Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders. CoRR abs/1509.06113 (2015) - [i9]Tianhao Zhang, Gregory Kahn, Sergey Levine, Pieter Abbeel:
Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search. CoRR abs/1509.06791 (2015) - [i8]Christopher Xie, Sachin Patil, Teodor Mihai Moldovan, Sergey Levine, Pieter Abbeel:
Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration. CoRR abs/1509.06824 (2015) - [i7]Justin Fu, Sergey Levine, Pieter Abbeel:
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors. CoRR abs/1509.06841 (2015) - 2013
- [i6]Ben Taskar, Pieter Abbeel, Daphne Koller:
Discriminative Probabilistic Models for Relational Data. CoRR abs/1301.0604 (2013) - [i5]Timothy Hunter, Aude Hofleitner, Jack Reilly, Walid Krichene, Jerome Thai, Anastasios Kouvelas, Pieter Abbeel, Alexandre M. Bayen:
Arriving on time: estimating travel time distributions on large-scale road networks. CoRR abs/1302.6617 (2013) - 2012
- [i4]Teodor Mihai Moldovan, Pieter Abbeel:
Safe Exploration in Markov Decision Processes. CoRR abs/1205.4810 (2012) - [i3]Pieter Abbeel, Daphne Koller, Andrew Y. Ng:
Learning Factor Graphs in Polynomial Time & Sample Complexity. CoRR abs/1207.1366 (2012) - [i2]Timothy Hunter, Tathagata Das, Matei Zaharia, Pieter Abbeel, Alexandre M. Bayen:
Large Scale Estimation in Cyberphysical Systems using Streaming Data: a Case Study with Smartphone Traces. CoRR abs/1212.3393 (2012) - 2011
- [i1]Timothy Hunter, Ryan Herring, Pieter Abbeel, Alexandre M. Bayen:
The path inference filter: model-based low-latency map matching of probe vehicle data. CoRR abs/1109.1966 (2011)
Coauthor Index
Jur P. van den Berg
aka: Jur van den Berg
aka: Jur van den Berg
[j21] [c323] [c305] [c303] [c301] [c282] [c225] [c199] [c198] [c184] [c172] [c170] [c168] [c167] [c164] [c155] [c145] [c137] [c114] [c107] [c95] [c92] [i324] [i304] [i302] [i290] [i238] [i167] [i154] [i146] [i144] [i137] [i112] [i85] [i70] [i65] [i62] [i46] [i43] [i41] [i36] [i34] [i32] [i24] [i23] [i21]
Kenneth Y. Goldberg
aka: Ken Goldberg
aka: Ken Goldberg
Joseph Gonzalez 0001
aka: Joseph E. Gonzalez
aka: Joseph E. Gonzalez
[c318] [c312] [c311] [c298] [c291] [c287] [c281] [c278] [c277] [c274] [c273] [c261] [c260] [c249] [c248] [c245] [c244] [c238] [c237] [c236] [c232] [c216] [c213] [i315] [i313] [i309] [i308] [i299] [i263] [i261] [i252] [i246] [i242] [i234] [i224] [i222] [i210] [i209] [i206] [i205] [i201] [i199] [i197] [i195] [i180] [i169] [i164] [i161] [i159]
[j25] [j24] [j23] [j18] [j17] [j14] [c340] [c336] [c330] [c324] [c292] [c242] [c215] [c211] [c204] [c203] [c202] [c195] [c192] [c190] [c189] [c185] [c182] [c173] [c165] [c163] [c161] [c160] [c159] [c158] [c154] [c153] [c151] [c148] [c143] [c140] [c138] [c136] [c133] [c130] [c129] [c127] [c126] [c125] [c124] [c123] [c122] [c119] [c108] [c104] [c103] [c102] [c101] [c98] [c97] [c93] [c90] [c89] [c88] [c82] [c81] [c80] [c74] [c66] [c65] [c52] [i333] [i332] [i331] [i328] [i325] [i310] [i300] [i251] [i233] [i218] [i216] [i188] [i160] [i153] [i148] [i141] [i135] [i125] [i121] [i117] [i116] [i114] [i110] [i107] [i106] [i105] [i104] [i103] [i101] [i97] [i95] [i94] [i92] [i90] [i87] [i86] [i84] [i81] [i79] [i78] [i77] [i72] [i61] [i58] [i55] [i53] [i40] [i39] [i38] [i37] [i35] [i31] [i29] [i28] [i27] [i26] [i22] [i19] [i18] [i16] [i15] [i12] [i11] [i10] [i9] [i8] [i7]
Stuart Russell 0001
aka: Stuart J. Russell
aka: Stuart J. Russell
John Schulman
aka: John D. Schulman
aka: John D. Schulman
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