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ORCIDPieter Abbeel
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- affiliation: University of California, Berkeley, USA
- affiliation: Stanford University, USA
- award (2013): Presidential Early Career Award for Scientists and Engineers
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2020 – today
- 2021
[j24]Gregory Kahn
, Pieter Abbeel, Sergey Levine:
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. IEEE Robotics Autom. Lett. 6(2): 1312-1319 (2021)- [j23]Gregory Kahn, Pieter Abbeel, Sergey Levine:
LaND: Learning to Navigate From Disengagements. IEEE Robotics Autom. Lett. 6(2): 1872-1879 (2021) - [i204]Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Michael Laskin:
Reinforcement Learning with Latent Flow. CoRR abs/2101.01857 (2021) - [i203]Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani:
Bottleneck Transformers for Visual Recognition. CoRR abs/2101.11605 (2021) - [i202]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) - [i201]Donald J. Hejna III, Pieter Abbeel, Lerrel Pinto:
Task-Agnostic Morphology Evolution. CoRR abs/2102.13100 (2021) - [i200]Lili Chen, Kimin Lee, Aravind Srinivas, Pieter Abbeel:
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings. CoRR abs/2103.02886 (2021) - [i199]Hao Liu, Pieter Abbeel:
Behavior From the Void: Unsupervised Active Pre-Training. CoRR abs/2103.04551 (2021) - [i198]Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch:
Pretrained Transformers as Universal Computation Engines. CoRR abs/2103.05247 (2021) - [i197]Rui Zhao, Yang Gao, Pieter Abbeel, Volker Tresp, Wei Xu:
Mutual Information State Intrinsic Control. CoRR abs/2103.08107 (2021) - [i196]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) - [i195]Ajay Jain, Matthew Tancik, Pieter Abbeel:
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis. CoRR abs/2104.00677 (2021) - [i194]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) - [i193]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) - [i192]David Lindner, Rohin Shah, Pieter Abbeel, Anca D. Dragan:
Learning What To Do by Simulating the Past. CoRR abs/2104.03946 (2021) - [i191]Yuqing Du, Olivia Watkins, Trevor Darrell, Pieter Abbeel, Deepak Pathak:
Auto-Tuned Sim-to-Real Transfer. CoRR abs/2104.07662 (2021) - [i190]Wilson Yan, Yunzhi Zhang, Pieter Abbeel, Aravind Srinivas:
VideoGPT: Video Generation using VQ-VAE and Transformers. CoRR abs/2104.10157 (2021) - 2020
- [c227]Ignasi Clavera, Yao Fu, Pieter Abbeel:
Model-Augmented Actor-Critic: Backpropagating through Paths. ICLR 2020 - [c226]Alexander C. Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel:
Sub-policy Adaptation for Hierarchical Reinforcement Learning. ICLR 2020 - [c225]Donald J. Hejna III, Lerrel Pinto, Pieter Abbeel:
Hierarchically Decoupled Imitation For Morphological Transfer. ICML 2020: 4159-4171 - [c224]Michael Laskin, Aravind Srinivas, Pieter Abbeel:
CURL: Contrastive Unsupervised Representations for Reinforcement Learning. ICML 2020: 5639-5650 - [c223]Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan, Peter Chen:
Variable Skipping for Autoregressive Range Density Estimation. ICML 2020: 6040-6049 - [c222]Kara Liu, Thanard Kurutach, Christine Tung, Pieter Abbeel, Aviv Tamar:
Hallucinative Topological Memory for Zero-Shot Visual Planning. ICML 2020: 6259-6270 - [c221]Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak:
Planning to Explore via Self-Supervised World Models. ICML 2020: 8583-8592 - [c220]Adam Stooke, Joshua Achiam, Pieter Abbeel:
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods. ICML 2020: 9133-9143 - [c219]Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra:
Plan2Vec: Unsupervised Representation Learning by Latent Plans. L4DC 2020: 935-946 - [c218]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 - [c217]Yuqing Du, Stas Tiomkin, Emre Kiciman, Daniel Polani, Pieter Abbeel, Anca D. Dragan:
AvE: Assistance via Empowerment. NeurIPS 2020 - [c216]Scott Emmons, Ajay Jain, Michael Laskin, Thanard Kurutach, Pieter Abbeel, Deepak Pathak:
Sparse Graphical Memory for Robust Planning. NeurIPS 2020 - [c215]Jonathan Ho, Ajay Jain, Pieter Abbeel:
Denoising Diffusion Probabilistic Models. NeurIPS 2020 - [c214]Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas:
Reinforcement Learning with Augmented Data. NeurIPS 2020 - [c213]Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine:
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model. NeurIPS 2020 - [c212]Alexander C. Li, Lerrel Pinto, Pieter Abbeel:
Generalized Hindsight for Reinforcement Learning. NeurIPS 2020 - [c211]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 - [c210]Yunzhi Zhang, Pieter Abbeel, Lerrel Pinto:
Automatic Curriculum Learning through Value Disagreement. NeurIPS 2020 - [c209]Ajay Jain, Pieter Abbeel, Deepak Pathak:
Locally Masked Convolution for Autoregressive Models. UAI 2020: 1358-1367 - [e2]Ken Goldberg, Pieter Abbeel, Kostas 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] - [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)
2010 – 2019
- 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, Peter Chen, Pieter Abbeel, Joseph M. Hellerstein, Sanjay Krishnan, Ion Stoica:
Deep Unsupervised Cardinality Estimation. Proc. VLDB Endow. 13(3): 279-292 (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 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, Peter 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 - [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 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: