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Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running.
Robust recovery controller for a quadrupedal robot using deep reinforcement learning
J. Lee, Jemin Hwangbo, and M. Hutter · 1901
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A markovian decision process
Richard Bellman · 1957
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Playback control of force teachable robots
Haruhiko Asada and Hideo Hanafusa · 1979
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Hopping in legged systems — modeling and simulation for the two-dimensional one-legged case
Marc H. Raibert · 1984
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Dynamic walk of a biped
Hirofumi Miura and Isao Shimoyama · 1984
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Learning from demonstration
Stefan Schaal · 1996
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Learning from demonstration
Stefan Schaal · 1997
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Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart J Russell, et al · 2000
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Learning agile robotic locomotion skills by imitating animals
X. Peng, Erwin Coumans, T. Zhang, T. Lee, J. Tan, and Sergey Levine · 2004
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Policy gradient reinforcement learning for fast quadrupedal locomotion
Nate Kohl and P. Stone · 2004
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Stochastic policy gradient reinforcement learning on a simple 3d biped
Russ Tedrake, T. Zhang, and H. Seung · 2004
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
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Learning cpg sensory feedback with policy gradient for biped locomotion for a full-body humanoid
G. Endo, J. Morimoto, Takamitsu Matsubara, J. Nakanishi, and G. Cheng · 2005
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A survey of robot learning from demonstration
Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2009
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Fast, robust quadruped locomotion over challenging terrain
Mrinal Kalakrishnan, Jonas Buchli, Peter Pastor, Michael N. Mistry, and Stefan Schaal · 2010
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Quadratic programming for inverse dynamics with optimal distribution of contact forces
Ludovic Righetti and Stefan Schaal · 2012
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Embedding active force control within the compliant hybrid zero dynamics to achieve stable, fast running on mabel
Koushil Sreenath, Hae won Park, Ioannis Poulakakis, and Jessy W. Grizzle · 2013
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Whole-body motion planning with centroidal dynamics and full kinematics
Hongkai Dai, Andrés Valenzuela, and Russ Tedrake · 2014
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Reinforcement learning with multi-fidelity simulators
Mark Cutler, Thomas J. Walsh, and Jonathan P. How · 2014
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Online planning for autonomous running jumps over obstacles in high-speed quadrupeds
Hae won Park, Patrick M. Wensing, and Sangbae Kim · 2015
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Anymal - a highly mobile and dynamic quadrupedal robot
M. Hutter, Christian Gehring, Dominic Jud, Andreas Lauber, Dario Bellicoso, Vassilios Tsounis, Jemin Hwangbo, K. Bodie, P. Fankhauser, Michael Bloesch, Remo Diethelm, Samuel Bachmann, A. Melzer, and M. Höpflinger · 2016
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Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot
Scott Kuindersma, Robin Deits, Maurice Fallon, Andrés Valenzuela, Hongkai Dai, Frank Permenter, Twan Koolen, Pat Marion, and Russ Tedrake · 2016
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Robot locomotion on hard and soft ground: Measuring stability and ground properties in-situ
Will Bosworth, Jonas Whitney, Sangbae Kim, and Neville Hogan · 2016
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Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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High-speed bounding with the mit cheetah 2: Control design and experiments
Hae-Won Park, Patrick M Wensing, and Sangbae Kim · 2017
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CAD 2 \text{CAD}^{2} RL: Real single-image flight without a single real image
Fereshteh Sadeghi and Sergey Levine · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Joshua Tobin, Rachel Fong, Alex Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
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Learning to reinforcement learn
Jane X. Wang, Zeb Kurth-Nelson, Hubert Soyer, Joel Z. Leibo, Dhruva Tirumala, Rémi Munos, Charles Blundell, Dharshan Kumaran, and Matthew M. Botvinick · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, P. Abbeel, and Sergey Levine · 2017
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Deep q-learning from demonstrations
Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John P. Agapiou, Joel Z. Leibo, and Audrunas Gruslys · 2017
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Overcoming exploration in reinforcement learning with demonstrations
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and P. Abbeel · 2017
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Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Matej Vecerík, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Manfred Otto Heess, Thomas Rothörl, Thomas Lampe, and Martin A. Riedmiller · 2017
A scalable approach to control diverse behaviors for physically simulated characters
Jungdam Won, Deepak Gopinath, and Jessica Hodgins · 2020
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Learning agile robotic locomotion skills by imitating animals
Xue Bin Peng, Erwin Coumans, Tingnan Zhang, Tsang-Wei Edward Lee, Jie Tan, and Sergey Levine · 2020
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Learning agile locomotion skills with a mentor
Atil Iscen, George Yu, Alejandro Escontrela, Deepali Jain, Jie Tan, and Ken Caluwaerts · 2020
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Learning agile locomotion via adversarial training
Yujin Tang, Jie Tan, and Tatsuya Harada · 2020
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Learning fast adaptation with meta strategy optimization
Wenhao Yu, J. Tan, Yunfei Bai, Erwin Coumans, and Sehoon Ha · 2020
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Rapidly adaptable legged robots via evolutionary meta-learning
Xingyou Song, Yuxiang Yang, Krzysztof Choromanski, Ken Caluwaerts, Wenbo Gao, Chelsea Finn, and Jie Tan · 2020
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Dynamic locomotion through online nonlinear motion optimization for quadrupedal robots
Dario Bellicoso, Fabian Jenelten, Christian Gehring, and Marco Hutter · 2018
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Walking and running with passive compliance: Lessons from engineering: A live demonstration of the atrias biped
Christian M. Hubicki, Andy Abate, Patrick Clary, Siavash Rezazadeh, Mikhail S. Jones, Andrew Peekema, Johnathan Van Why, Ryan Domres, Albert Wu, William C. Martin, Hartmut Geyer, and Jonathan W. Hurst · 2018
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Mit cheetah 3: Design and control of a robust, dynamic quadruped robot
G. Bledt, Matthew J. Powell, B. Katz, J. Carlo, P. Wensing, and Sangbae Kim · 2018
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Gait and trajectory optimization for legged systems through phase-based end-effector parameterization
Alexander W Winkler, C Dario Bellicoso, Marco Hutter, and Jonas Buchli · 2018
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Learning basketball dribbling skills using trajectory optimization and deep reinforcement learning
L. Liu and J. Hodgins · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
J. Tan, T. Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and V. Vanhoucke · 2018
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Sim-to-real transfer of robotic control with dynamics randomization
X. Peng, Marcin Andrychowicz, W. Zaremba, and P. Abbeel · 2018
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Deep deterministic policy gradient (ddpg)-based resource allocation scheme for noma vehicular communications
Yi-Han Xu, Cheng-Cheng Yang, Min Hua, and Wen Zhou · 2020
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Avoiding side effects in complex environments
Alexander Matt Turner, Neale Ratzlaff, and Prasad Tadepalli · 2020
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The mit humanoid robot: Design, motion planning, and control for acrobatic behaviors
Matthew Chignoli, Donghyun Kim, Elijah Stanger-Jones, and Sangbae Kim · 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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Jumping over obstacles with mit cheetah 2
Hae won Park, Patrick M. Wensing, and Sangbae Kim · 2021
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Learning to jump from pixels
G. Margolis, Tao Chen, Kartik Paigwar, Xiang Fu, Donghyun Kim, Sangbae Kim, and Pulkit Agrawal · 2021
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Supertrack: Motion tracking for physically simulated characters using supervised learning
Levi Fussell, Kevin Bergamin, and Daniel Holden · 2021
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Amp: Adversarial motion priors for stylized physics-based character control
Xue Bin Peng, Ze Ma, P. Abbeel, Sergey Levine, and Angjoo Kanazawa · 2021
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Model-free reinforcement learning for robust locomotion using demonstrations from trajectory optimization
Miroslav Bogdanovic, Majid Khadiv, and Ludovic Righetti · 2021
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Lifelong robotic reinforcement learning by retaining experiences
Annie Xie and Chelsea Finn · 2021
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Pybullet, a python module for physics simulation for games, robotics and machine learning
Erwin Coumans and Yunfei Bai · 2021
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Legged locomotion in challenging terrains using egocentric vision
Ananye Agarwal, Ashish Kumar, Jitendra Malik, and Deepak Pathak · 2022
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Advanced skills through multiple adversarial motion priors in reinforcement learning
Eric Vollenweider, Marko Bjelonic, Victor Klemm, N. Rudin, Joonho Lee, and Marco Hutter · 2022
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Multi-modal legged locomotion framework with automated residual reinforcement learning
Chenxiao Yu and Andre Rosendo · 2022
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Opt-mimic: Imitation of optimized trajectories for dynamic quadruped behaviors
Yuni Fuchioka, Zhaoming Xie, and Michiel van de Panne · 2022
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Adversarial motion priors make good substitutes for complex reward functions
Alejandro Escontrela, Xue Bin Peng, Wenhao Yu, Tingnan Zhang, Atil Iscen, Ken Goldberg, and P. Abbeel · 2022
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Rapid locomotion via reinforcement learning
Gabriel Margolis, Ge Yang, Kartik Paigwar, Tao Chen, and Pulkit Agrawal · 2022
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On pathologies in kl-regularized reinforcement learning from expert demonstrations
Tim G. J. Rudner and Cong Lu · 2022
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Dropout q-functions for doubly efficient reinforcement learning
Takuya Hiraoka, Takahisa Imagawa, Taisei Hashimoto, Takashi Onishi, and Yoshimasa Tsuruoka · 2022
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Safe reinforcement learning for legged locomotion
Tsung-Yen Yang, Tingnan Zhang, Linda Luu, Sehoon Ha, Jie Tan, and Wenhao Yu · 2022
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A walk in the park: Learning to walk in 20 minutes with model-free reinforcement learning
Laura Smith, Ilya Kostrikov, and Sergey Levine · 2022
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