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2020 – today
- 2022
[j9]Dylan P. Losey, Hong Jun Jeon, Mengxi Li, Krishnan Srinivasan
, Ajay Mandlekar, Animesh Garg, Jeannette Bohg, Dorsa Sadigh:
Learning latent actions to control assistive robots. Auton. Robots 46(1): 115-147 (2022)- [j8]Aysegul Dundar, Kevin J. Shih, Animesh Garg, Robert Pottorf, Anrew Tao, Bryan Catanzaro:
Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos. IEEE Trans. Pattern Anal. Mach. Intell. 44(7): 3883-3894 (2022) - [j7]Jiankai Sun, De-An Huang, Bo Lu, Yun-Hui Liu, Bolei Zhou, Animesh Garg:
PlaTe: Visually-Grounded Planning With Transformers in Procedural Tasks. IEEE Robotics Autom. Lett. 7(2): 4924-4930 (2022) - [c64]Qizhen Zhang, Chris Lu, Animesh Garg, Jakob N. Foerster:
Centralized Model and Exploration Policy for Multi-Agent RL. AAMAS 2022: 1500-1508 - [c63]Samarth Sinha, Jiaming Song, Animesh Garg, Stefano Ermon:
Experience Replay with Likelihood-free Importance Weights. L4DC 2022: 110-123 - [i81]Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhihong Deng, Animesh Garg, Peng Liu, Zhaoran Wang:
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning. CoRR abs/2202.11566 (2022) - [i80]Eric Heiden, Miles Macklin, Yashraj S. Narang, Dieter Fox, Animesh Garg, Fabio Ramos:
DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting. CoRR abs/2203.10263 (2022) - [i79]Satya Krishna Gorti, Noel Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, Guang Wei Yu:
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval. CoRR abs/2203.15086 (2022) - [i78]Claas Voelcker, Victor Liao, Animesh Garg, Amir-massoud Farahmand:
Value Gradient weighted Model-Based Reinforcement Learning. CoRR abs/2204.01464 (2022) - [i77]Jie Xu, Viktor Makoviychuk, Yashraj S. Narang, Fabio T. Ramos, Wojciech Matusik, Animesh Garg, Miles Macklin:
Accelerated Policy Learning with Parallel Differentiable Simulation. CoRR abs/2204.07137 (2022) - [i76]Yun-Chun Chen, Haoda Li, Dylan Turpin, Alec Jacobson, Animesh Garg:
Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors. CoRR abs/2205.14886 (2022) - [i75]Mohan Zhang, Xiaozhou Wang, Benjamin Decardi-Nelson, Bo Song, An Zhang, Jinfeng Liu, Sile Tao, Jiayi Cheng, Xiaohong Liu, DengDeng Yu, Matthew Poon, Animesh Garg:
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments. CoRR abs/2206.08851 (2022) - 2021
- [c62]Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti:
DIBS: Diversity Inducing Information Bottleneck in Model Ensembles. AAAI 2021: 9666-9674 - [c61]Valts Blukis, Chris Paxton, Dieter Fox, Animesh Garg, Yoav Artzi:
A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Execution. CoRL 2021: 706-717 - [c60]Haoping Xu, Yi Ru Wang, Sagi Eppel, Alán Aspuru-Guzik, Florian Shkurti, Animesh Garg:
Seeing Glass: Joint Point-Cloud and Depth Completion for Transparent Objects. CoRL 2021: 827-838 - [c59]Samarth Sinha, Ajay Mandlekar, Animesh Garg:
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning in Robotics. CoRL 2021: 907-917 - [c58]Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg:
Conservative Safety Critics for Exploration. ICLR 2021 - [c57]Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg:
C-Learning: Horizon-Aware Cumulative Accessibility Estimation. ICLR 2021 - [c56]Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, Florian Shkurti:
Latent Skill Planning for Exploration and Transfer. ICLR 2021 - [c55]Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang:
Principled Exploration via Optimistic Bootstrapping and Backward Induction. ICML 2021: 577-587 - [c54]Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Anima Anandkumar:
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition. ICML 2021: 6860-6870 - [c53]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Value Iteration in Continuous Actions, States and Time. ICML 2021: 7224-7234 - [c52]Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar:
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning. ICML 2021: 7301-7312 - [c51]Homanga Bharadhwaj, Animesh Garg, Florian Shkurti:
LEAF: Latent Exploration Along the Frontier. ICRA 2021: 677-684 - [c50]Zhaoming Xie, Xingye Da, Michiel van de Panne, Buck Babich, Animesh Garg:
Dynamics Randomization Revisited: A Case Study for Quadrupedal Locomotion. ICRA 2021: 4955-4961 - [c49]Arthur Allshire, Roberto Martín-Martín, Charles Lin, Shawn Manuel, Silvio Savarese, Animesh Garg:
LASER: Learning a Latent Action Space for Efficient Reinforcement Learning. ICRA 2021: 6650-6656 - [c48]Xinlei Pan, Animesh Garg, Animashree Anandkumar, Yuke Zhu:
Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects. ICRA 2021: 7540-7547 - [c47]Haoyu Xiong, Quanzhou Li, Yun-Chun Chen, Homanga Bharadhwaj, Samarth Sinha, Animesh Garg:
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos. IROS 2021: 7827-7834 - [c46]Michael Poli, Stefano Massaroli, Luca Scimeca, Sanghyuk Chun, Seong Joon Oh, Atsushi Yamashita, Hajime Asama, Jinkyoo Park, Animesh Garg:
Neural Hybrid Automata: Learning Dynamics With Multiple Modes and Stochastic Transitions. NeurIPS 2021: 9977-9989 - [c45]Nikita Dvornik, Isma Hadji, Konstantinos G. Derpanis, Animesh Garg, Allan D. Jepson:
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers. NeurIPS 2021: 13782-13793 - [c44]Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, Zhaoran Wang:
Dynamic Bottleneck for Robust Self-Supervised Exploration. NeurIPS 2021: 17007-17020 - [c43]Eric Heiden, Miles Macklin, Yashraj S. Narang, Dieter Fox, Animesh Garg, Fabio Ramos:
DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting. Robotics: Science and Systems 2021 - [c42]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Robust Value Iteration for Continuous Control Tasks. Robotics: Science and Systems 2021 - [c41]Dylan Turpin, Liquan Wang, Stavros Tsogkas, Sven J. Dickinson, Animesh Garg:
GIFT: Generalizable Interaction-aware Functional Tool Affordances without Labels. Robotics: Science and Systems 2021 - [i74]Haoyu Xiong, Quanzhou Li, Yun-Chun Chen, Homanga Bharadhwaj, Samarth Sinha, Animesh Garg:
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos. CoRR abs/2101.07241 (2021) - [i73]Samarth Sinha, Animesh Garg:
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning. CoRR abs/2103.06326 (2021) - [i72]Mayank Mittal, David Hoeller, Farbod Farshidian, Marco Hutter, Animesh Garg:
Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation. CoRR abs/2103.10534 (2021) - [i71]Arthur Allshire, Roberto Martín-Martín, Charles Lin, Shawn Manuel, Silvio Savarese, Animesh Garg:
LASER: Learning a Latent Action Space for Efficient Reinforcement Learning. CoRR abs/2103.15793 (2021) - [i70]Zhaoming Xie, Xingye Da, Buck Babich, Animesh Garg, Michiel van de Panne:
GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model. CoRR abs/2104.09771 (2021) - [i69]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Value Iteration in Continuous Actions, States and Time. CoRR abs/2105.04682 (2021) - [i68]Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang:
Principled Exploration via Optimistic Bootstrapping and Backward Induction. CoRR abs/2105.06022 (2021) - [i67]Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Animashree Anandkumar:
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition. CoRR abs/2105.08692 (2021) - [i66]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Robust Value Iteration for Continuous Control Tasks. CoRR abs/2105.12189 (2021) - [i65]Eric Heiden, Miles Macklin, Yashraj S. Narang, Dieter Fox, Animesh Garg, Fabio Ramos:
DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting. CoRR abs/2105.12244 (2021) - [i64]Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar:
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning. CoRR abs/2106.00136 (2021) - [i63]Michael Poli, Stefano Massaroli, Luca Scimeca, Seong Joon Oh, Sanghyuk Chun, Atsushi Yamashita, Hajime Asama, Jinkyoo Park, Animesh Garg:
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions. CoRR abs/2106.04165 (2021) - [i62]Dylan Turpin, Liquan Wang, Stavros Tsogkas, Sven J. Dickinson, Animesh Garg:
GIFT: Generalizable Interaction-aware Functional Tool Affordances without Labels. CoRR abs/2106.14973 (2021) - [i61]Dylan P. Losey, Hong Jun Jeon, Mengxi Li, Krishnan Srinivasan, Ajay Mandlekar, Animesh Garg, Jeannette Bohg, Dorsa Sadigh:
Learning Latent Actions to Control Assistive Robots. CoRR abs/2107.02907 (2021) - [i60]Valts Blukis, Chris Paxton, Dieter Fox, Animesh Garg, Yoav Artzi:
A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Execution. CoRR abs/2107.05612 (2021) - [i59]Qizhen Zhang, Chris Lu, Animesh Garg, Jakob N. Foerster:
Centralized Model and Exploration Policy for Multi-Agent RL. CoRR abs/2107.06434 (2021) - [i58]Arthur Allshire, Mayank Mittal, Varun Lodaya, Viktor Makoviychuk, Denys Makoviichuk, Felix Widmaier, Manuel Wüthrich, Stefan Bauer, Ankur Handa, Animesh Garg:
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger. CoRR abs/2108.09779 (2021) - [i57]Nikita Dvornik, Isma Hadji, Konstantinos G. Derpanis, Animesh Garg, Allan D. Jepson:
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers. CoRR abs/2108.11996 (2021) - [i56]Jiankai Sun, De-An Huang, Bo Lu, Yun-Hui Liu, Bolei Zhou, Animesh Garg:
PlaTe: Visually-Grounded Planning with Transformers in Procedural Tasks. CoRR abs/2109.04869 (2021) - [i55]Stefan Bauer, Felix Widmaier, Manuel Wüthrich, Niklas Funk, Julen Urain De Jesus, Jan Peters, Joe Watson, Claire Chen, Krishnan Srinivasan, Junwu Zhang, Jeffrey Zhang, Matthew R. Walter, Rishabh Madan, Charles B. Schaff, Takahiro Maeda, Takuma Yoneda, Denis Yarats, Arthur Allshire, Ethan K. Gordon, Tapomayukh Bhattacharjee, Siddhartha S. Srinivasa, Animesh Garg, Annika Buchholz, Sebastian Stark, Thomas Steinbrenner, Joel Akpo, Shruti Joshi, Vaibhav Agrawal, Bernhard Schölkopf:
A Robot Cluster for Reproducible Research in Dexterous Manipulation. CoRR abs/2109.10957 (2021) - [i54]Homanga Bharadhwaj, De-An Huang, Chaowei Xiao, Anima Anandkumar, Animesh Garg:
Auditing AI models for Verified Deployment under Semantic Specifications. CoRR abs/2109.12456 (2021) - [i53]Haoping Xu, Yi Ru Wang, Sagi Eppel, Alán Aspuru-Guzik, Florian Shkurti, Animesh Garg:
Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects. CoRR abs/2110.00087 (2021) - [i52]Michael Lutter, Boris Belousov, Shie Mannor, Dieter Fox, Animesh Garg, Jan Peters:
Continuous-Time Fitted Value Iteration for Robust Policies. CoRR abs/2110.01954 (2021) - [i51]Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, Zhaoran Wang:
Dynamic Bottleneck for Robust Self-Supervised Exploration. CoRR abs/2110.10735 (2021) - [i50]Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar:
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions. CoRR abs/2110.14538 (2021) - [i49]Matthew Shunshi Zhang, Murat Erdogdu, Animesh Garg:
Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings. CoRR abs/2111.00185 (2021) - [i48]Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Yoshinobu Kawahara:
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics. CoRR abs/2111.01365 (2021) - 2020
- [j6]Kuan Fang, Yuke Zhu, Animesh Garg, Andrey Kurenkov, Viraj Mehta, Li Fei-Fei, Silvio Savarese:
Learning task-oriented grasping for tool manipulation from simulated self-supervision. Int. J. Robotics Res. 39(2-3) (2020) - [j5]Vinu Joseph, Ganesh Gopalakrishnan, Saurav Muralidharan, Michael Garland, Animesh Garg:
A Programmable Approach to Neural Network Compression. IEEE Micro 40(5): 17-25 (2020) - [j4]Michelle A. Lee, Yuke Zhu, Peter Zachares, Matthew Tan, Krishnan Srinivasan, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg:
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks. IEEE Trans. Robotics 36(3): 582-596 (2020) - [c40]Xingye Da, Zhaoming Xie, David Hoeller, Byron Boots, Anima Anandkumar, Yuke Zhu, Buck Babich, Animesh Garg:
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion. CoRL 2020: 883-894 - [c39]Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar:
Angular Visual Hardness. ICML 2020: 1637-1648 - [c38]Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B. Patel, Animashree Anandkumar:
Semi-Supervised StyleGAN for Disentanglement Learning. ICML 2020: 7360-7369 - [c37]Dylan P. Losey, Krishnan Srinivasan, Ajay Mandlekar, Animesh Garg, Dorsa Sadigh:
Controlling Assistive Robots with Learned Latent Actions. ICRA 2020: 378-384 - [c36]Ajay Mandlekar, Fabio Ramos, Byron Boots, Silvio Savarese, Li Fei-Fei, Animesh Garg, Dieter Fox:
IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data. ICRA 2020: 4414-4420 - [c35]De-An Huang, Yu-Wei Chao, Chris Paxton, Xinke Deng, Li Fei-Fei, Juan Carlos Niebles, Animesh Garg, Dieter Fox:
Motion Reasoning for Goal-Based Imitation Learning. ICRA 2020: 4878-4884 - [c34]Michelle A. Lee, Carlos Florensa, Jonathan Tremblay, Nathan D. Ratliff, Animesh Garg, Fabio Ramos, Dieter Fox:
Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning. ICRA 2020: 7505-7512 - [c33]Andrey Kurenkov, Joseph Taglic, Rohun Kulkarni, Marcus Dominguez-Kuhne, Animesh Garg, Roberto Martín-Martín, Silvio Savarese:
Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter. IROS 2020: 8408-8414 - [c32]Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg:
Causal Discovery in Physical Systems from Videos. NeurIPS 2020 - [c31]Silviu Pitis, Elliot Creager, Animesh Garg:
Counterfactual Data Augmentation using Locally Factored Dynamics. NeurIPS 2020 - [c30]Samarth Sinha, Animesh Garg, Hugo Larochelle:
Curriculum By Smoothing. NeurIPS 2020 - [c29]Hongyu Ren, Yuke Zhu, Jure Leskovec, Animashree Anandkumar, Animesh Garg:
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation. UAI 2020: 1378-1387 - [i47]Aysegul Dundar, Kevin J. Shih, Animesh Garg, Robert Pottorf, Andrew Tao, Bryan Catanzaro:
Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos. CoRR abs/2001.09518 (2020) - [i46]Samarth Sinha, Animesh Garg, Hugo Larochelle:
Curriculum By Texture. CoRR abs/2003.01367 (2020) - [i45]Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B. Patel, Anima Anandkumar:
Semi-Supervised StyleGAN for Disentanglement Learning. CoRR abs/2003.03461 (2020) - [i44]Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti:
DIBS: Diversity inducing Information Bottleneck in Model Ensembles. CoRR abs/2003.04514 (2020) - [i43]Michelle A. Lee, Carlos Florensa, Jonathan Tremblay, Nathan D. Ratliff, Animesh Garg, Fabio Ramos, Dieter Fox:
Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning. CoRR abs/2005.10872 (2020) - [i42]Homanga Bharadhwaj, Animesh Garg, Florian Shkurti:
Dynamics-Aware Latent Space Reachability for Exploration in Temporally-Extended Tasks. CoRR abs/2005.10934 (2020) - [i41]Samarth Sinha, Jiaming Song, Animesh Garg, Stefano Ermon:
Experience Replay with Likelihood-free Importance Weights. CoRR abs/2006.13169 (2020) - [i40]Samarth Sinha, Anirudh Goyal, Animesh Garg:
Maximum Entropy Models for Fast Adaptation. CoRR abs/2006.16524 (2020) - [i39]Homanga Bharadhwaj, Dylan Turpin, Animesh Garg, Ashton Anderson:
De-anonymization of authors through arXiv submissions during double-blind review. CoRR abs/2007.00177 (2020) - [i38]Yunzhu Li, Antonio Torralba, Animashree Anandkumar, Dieter Fox, Animesh Garg:
Causal Discovery in Physical Systems from Videos. CoRR abs/2007.00631 (2020) - [i37]Silviu Pitis, Elliot Creager, Animesh Garg:
Counterfactual Data Augmentation using Locally Factored Dynamics. CoRR abs/2007.02863 (2020) - [i36]Andrey Kurenkov, Joseph Taglic, Rohun Kulkarni, Marcus Dominguez-Kuhne, Animesh Garg, Roberto Martín-Martín, Silvio Savarese:
Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter. CoRR abs/2008.06073 (2020) - [i35]Hongyu Ren, Yuke Zhu, Jure Leskovec, Anima Anandkumar, Animesh Garg:
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation. CoRR abs/2008.07087 (2020) - [i34]Tim D. Barfoot, Jessica Burgner-Kahrs, Eric D. Diller, Animesh Garg, Andrew A. Goldenberg, Jonathan Kelly, Xinyu Liu, Hani E. Naguib, Goldie Nejat, Angela P. Schoellig, Florian Shkurti, Hallie Siegel, Yu Sun, Steven L. Waslander:
Making Sense of the Robotized Pandemic Response: A Comparison of Global and Canadian Robot Deployments and Success Factors. CoRR abs/2009.08577 (2020) - [i33]Xingye Da, Zhaoming Xie, David Hoeller, Byron Boots, Animashree Anandkumar, Yuke Zhu, Buck Babich, Animesh Garg:
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion. CoRR abs/2009.10019 (2020) - [i32]Samarth Sinha, Homanga Bharadhwaj, Aravind Srinivas, Animesh Garg:
D2RL: Deep Dense Architectures in Reinforcement Learning. CoRR abs/2010.09163 (2020) - [i31]Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg:
Conservative Safety Critics for Exploration. CoRR abs/2010.14497 (2020) - [i30]Zhaoming Xie, Xingye Da, Michiel van de Panne, Buck Babich, Animesh Garg:
Dynamics Randomization Revisited: A Case Study for Quadrupedal Locomotion. CoRR abs/2011.02404 (2020) - [i29]Augustin Harter, Andrew Melnik, Gaurav Kumar, Dhruv Agarwal, Animesh Garg, Helge J. Ritter:
Solving Physics Puzzles by Reasoning about Paths. CoRR abs/2011.07357 (2020) - [i28]Wei Yu, Wenxin Chen, Steve Easterbrook, Animesh Garg:
Action Concept Grounding Network for Semantically-Consistent Video Generation. CoRR abs/2011.11201 (2020) - [i27]Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg:
C-Learning: Horizon-Aware Cumulative Accessibility Estimation. CoRR abs/2011.12363 (2020) - [i26]Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, Florian Shkurti:
Skill Transfer via Partially Amortized Hierarchical Planning. CoRR abs/2011.13897 (2020) - [i25]Xinlei Pan, Animesh Garg, Animashree Anandkumar, Yuke Zhu:
Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects. CoRR abs/2012.12209 (2020)
2010 – 2019
- 2019
- [j3]Sanjay Krishnan, Animesh Garg, Richard Liaw, Brijen Thananjeyan, Lauren Miller, Florian T. Pokorny, Ken Goldberg:
SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards. Int. J. Robotics Res. 38(2-3) (2019) - [c28]Kuan Fang, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei:
Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation. CoRL 2019: 42-52 - [c27]Andrey Kurenkov, Ajay Mandlekar, Roberto Martin Martin, Silvio Savarese, Animesh Garg:
AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers. CoRL 2019: 717-734 - [c26]De-An Huang, Suraj Nair, Danfei Xu, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, Juan Carlos Niebles:
Neural Task Graphs: Generalizing to Unseen Tasks From a Single Video Demonstration. CVPR 2019: 8565-8574 - [c25]Michael Danielczuk, Andrey Kurenkov, Ashwin Balakrishna, Matthew Matl, David Wang, Roberto Martín-Martín, Animesh Garg, Silvio Savarese, Ken Goldberg:
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter. ICRA 2019: 1614-1621 - [c24]Michelle A. Lee, Yuke Zhu, Krishnan Srinivasan, Parth Shah, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg:
Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks. ICRA 2019: 8943-8950 - [c23]Roberto Martín-Martín, Michelle A. Lee, Rachel Gardner, Silvio Savarese, Jeannette Bohg, Animesh Garg:
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks. IROS 2019: 1010-1017 - [c22]Ajay Mandlekar, Jonathan Booher, Max Spero, Albert Tung, Anchit Gupta, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei:
Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity. IROS 2019: 1048-1055 - [c21]De-An Huang, Danfei Xu, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei, Juan Carlos Niebles:
Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning. IROS 2019: 2635-2642 - [i24]Michael Danielczuk, Andrey Kurenkov, Ashwin Balakrishna, Matthew Matl, David Wang, Roberto Martín-Martín, Animesh Garg, Silvio Savarese, Ken Goldberg:
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter. CoRR abs/1903.01588 (2019) - [i23]Roberto Martín-Martín, Michelle A. Lee, Rachel Gardner, Silvio Savarese, Jeannette Bohg, Animesh Garg:
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks. CoRR abs/1906.08880 (2019) - [i22]Michelle A. Lee, Yuke Zhu, Peter Zachares, Matthew Tan, Krishnan Srinivasan, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg:
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks. CoRR abs/1907.13098 (2019) - [i21]De-An Huang, Danfei Xu, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei, Juan Carlos Niebles:
Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning. CoRR abs/1908.06769 (2019) - [i20]Kevin J. Shih, Aysegul Dundar, Animesh Garg, Robert Pottorf, Andrew Tao, Bryan Catanzaro:
Video Interpolation and Prediction with Unsupervised Landmarks. CoRR abs/1909.02749 (2019) - [i19]Andrey Kurenkov, Ajay Mandlekar, Roberto Martin Martin, Silvio Savarese, Animesh Garg:
AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers. CoRR abs/1909.04121 (2019) - [i18]Dylan P. Losey, Krishnan Srinivasan, Ajay Mandlekar, Animesh Garg, Dorsa Sadigh:
Controlling Assistive Robots with Learned Latent Actions. CoRR abs/1909.09674 (2019) - [i17]Kuan Fang, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei:
Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation. CoRR abs/1910.13395 (2019) - [i16]