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Representation learning and unsupervised skill discovery can allow robots to acquire diverse and reusable behaviors without the need for task-specific rewards.
Fast Task Inference with Variational Intrinsic Successor Features, January 2020
Steven Hansen, Will Dabney, Andre Barreto, Tom Van de Wiele, David Warde-Farley, and Volodymyr Mnih · 1906
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Dynamics-Aware Unsupervised Discovery of Skills, February 2020b
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 1907
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Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning, April 2020a
Archit Sharma, Michael Ahn, Sergey Levine, Vikash Kumar, Karol Hausman, and Shixiang Gu · 2004
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Multi-expert learning of adaptive legged locomotion
Chuanyu Yang, Kai Yuan, Qiuguo Zhu, Wanming Yu, and Zhibin Li · 2012
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Empowerment – an Introduction, October 2013
Christoph Salge, Cornelius Glackin, and Daniel Polani · 2013
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Variational Intrinsic Control
Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 2017
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Proximal Policy Optimization Algorithms, August 2017
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Variational Option Discovery Algorithms, July 2018
Joshua Achiam, Harrison Edwards, Dario Amodei, and Pieter Abbeel · 2018
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Diversity is All You Need: Learning Skills without a Reward Function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Learning agile and dynamic motor skills for legged robots
Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso, Vassilios Tsounis, Vladlen Koltun, and Marco Hutter · 2019
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Learning quadrupedal locomotion over challenging terrain
Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter · 2020
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Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
Jongwook Choi, Archit Sharma, Honglak Lee, Sergey Levine, and Shixiang Shane Gu · 2021
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RMA: Rapid Motor Adaptation for Legged Robots
Ashish Kumar, Zipeng Fu, Deepak Pathak, and Jitendra Malik · 2021
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APS: Active Pretraining with Successor Features, August 2021
Hao Liu and Pieter Abbeel · 2021
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Isaac Gym: High Performance GPU Based Physics Simulation For Robot Learning
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, and Gavriel State · 2021
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Lipschitz-constrained Unsupervised Skill Discovery
Seohong Park, Jongwook Choi, Jaekyeom Kim, Honglak Lee, and Gunhee Kim · 2021
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AMP: adversarial motion priors for stylized physics-based character control
Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, and Angjoo Kanazawa · 2021
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Learning more skills through optimistic exploration
D. J. Strouse, Kate Baumli, David Warde-Farley, Volodymyr Mnih, and Steven Stenberg Hansen · 2021
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Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots
Zipeng Fu, Ashish Kumar, Jitendra Malik, and Deepak Pathak · 2022
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ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots, June 2023
David Hoeller, Nikita Rudin, Dhionis Sako, and Marco Hutter · 2023
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Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions
Chenhao Li, Sebastian Blaes, Pavel Kolev, Marin Vlastelica, Jonas Frey, and Georg Martius · 2023
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Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Ritvik Singh, Yunrong Guo, Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, and Animesh Garg · 2023
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METRA: Scalable Unsupervised RL with Metric-Aware Abstraction
Seohong Park, Oleh Rybkin, and Sergey Levine · 2023
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Neural Volumetric Memory for Visual Locomotion Control, April 2023
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Siddhant Gangapurwala, Mathieu Geisert, Romeo Orsolino, Maurice Fallon, and Ioannis Havoutis · 2022
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Unsupervised Reinforcement Learning with Contrastive Intrinsic Control
Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, and Pieter Abbeel · 2022
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Learning Visual Locomotion with Cross-Modal Supervision, November 2022
Antonio Loquercio, Ashish Kumar, and Jitendra Malik · 2022
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Learning robust perceptive locomotion for quadrupedal robots in the wild
Takahiro Miki, Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter · 2022
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ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters
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Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality
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An Information-Theoretic Perspective on Intrinsic Motivation in Reinforcement Learning: A Survey
Arthur Aubret, Laetitia Matignon, and Salima Hassas · 2023
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Ruihan Yang, Ge Yang, and Xiaolong Wang · 2023
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Identifying important sensory feedback for learning locomotion skills
Wanming Yu, Chuanyu Yang, Christopher McGreavy, Eleftherios Triantafyllidis, Guillaume Bellegarda, Milad Shafiee, Auke Jan Ijspeert, and Zhibin Li · 2023
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Robot Parkour Learning, September 2023
Ziwen Zhuang, Zipeng Fu, Jianren Wang, Christopher Atkeson, Soeren Schwertfeger, Chelsea Finn, and Hang Zhao · 2023
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Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-free Design, March 2024
Vassil Atanassov, Jiatao Ding, Jens Kober, Ioannis Havoutis, and Cosimo Della Santina · 2024
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Learning Diverse Skills for Local Navigation under Multi-constraint Optimality
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Extreme Parkour with Legged Robots
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Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control, January 2024
Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, and Koushil Sreenath · 2024
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Rapid locomotion via reinforcement learning
Gabriel B. Margolis, Ge Yang, Kartik Paigwar, Tao Chen, and Pulkit Agrawal · 2024
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Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End
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