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We study the problem of realizing the full spectrum of bipedal locomotion on a real robot with sim-to-real reinforcement learning (RL).
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“Learning Memory-Based Control for Human-Scale Bipedal Locomotion”, 2020
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Xue Peng, Pieter Abbeel, Sergey Levine and Michiel van Panne · 2018
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Zhenyu Gan, Yevgeniy Yesilevskiy, Petr Zaytsev and C Remy · 2018
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“Sim-to-Real Transfer of Robotic Control with Dynamics Randomization”
X.. Peng, M. Andrychowicz, W. Zaremba and P. Abbeel · 2018
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“cassie-mujoco-sim”, 2018
Agility Robotics · 2018
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Jemin Hwangbo et al · 2019
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“Neural state machine for character-scene interactions.”, 2019
Sebastian Starke, He Zhang, Taku Komura and Jun Saito · 2019
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“DReCon: data-driven responsive control of physics-based characters”
Kevin Bergamin, Simon Clavet, Daniel Holden and James Forbes · 2019
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“On Learning Symmetric Locomotion”
Farzad Adbolhosseini et al · 2019
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Yuval Tassa et al · 2018
Cited alongside, same era.
“Learning to walk via deep reinforcement learning”
Tuomas Haarnoja et al · 2018
Cited alongside, same era.
“Sim-to-Real: Learning Agile Locomotion For Quadruped Robots”
Jie Tan et al · 2018
Cited alongside, same era.
“Reinforcement learning: An introduction”
Richard Sutton and Andrew Barto · 2018
Cited alongside, same era.
“Interactive Character Animation using Simulated Physics.”
Thomas Geijtenbeek, Nicolas Pronost, Arjan Egges and Mark Overmars
Cited in the paper.
“Sim-to-Real: Learning Agile Locomotion For Quadruped Robots”
Jie Tan et al
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“Local motion phases for learning multi-contact character movements”
Sebastian Starke, Yiwei Zhao, Taku Komura and Kazi Zaman · 2020
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“Learning Locomotion Skills for Cassie: Iterative Design and Sim-to-Real” 100
Zhaoming Xie et al · 2020
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