2019 International Conference on Robotics and Automation (ICRA) 2019
DOI: 10.1109/icra.2019.8793627
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Reinforcement Learning Meets Hybrid Zero Dynamics: A Case Study for RABBIT

Abstract: The design of feedback controllers for bipedal robots is challenging due to the hybrid nature of its dynamics and the complexity imposed by high-dimensional bipedal models. In this paper, we present a novel approach for the design of feedback controllers using Reinforcement Learning (RL) and Hybrid Zero Dynamics (HZD). Existing RL approaches for bipedal walking are inefficient as they do not consider the underlying physics, often requires substantial training, and the resulting controller may not be applicable… Show more

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Cited by 20 publications
(19 citation statements)
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“…In this section, we build upon our previous work proposed in [11], [13] to implement a cascade-structure learning framework that realizes stable and robust walking gaits for the 3D bipedal robots. The specific design ensures successful transferring learned policies in simulation to robot hardware with minimal turning.…”
Section: Learning Approachmentioning
confidence: 99%
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“…In this section, we build upon our previous work proposed in [11], [13] to implement a cascade-structure learning framework that realizes stable and robust walking gaits for the 3D bipedal robots. The specific design ensures successful transferring learned policies in simulation to robot hardware with minimal turning.…”
Section: Learning Approachmentioning
confidence: 99%
“…Foot placement regulation controller has been widely used in 3D bipedal walking robots with the objective of improving the speed tracking and the stability and robustness of the walking gait [20]- [22]. Longitudinal speed regulation, defined by (13), sets a target offset in the swing hip pitch joint, whereas lateral speed regulation (11) do the same for the swing hip roll angle. Direction regulation (12) add an offset to the yaw hip angle to keep the torso yaw orientation at the desired angle.…”
Section: B Feedback Regulationsmentioning
confidence: 99%
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“…To circumvent this engineering empiricism, the field of machine learning has approached bipedal locomotion from many perspectives, including reinforcement leaning and imitation learning. Reinforcement learning simplifies the process of "learning to walk" [13] without prior knowledge [14]- [17], but because this method relies on a carefully crafted reward function, the behavior is exclusively determined by its construction. This motivates the second method, imitation learning, which infers the underlying reward function from expert demonstrations [18]- [20].…”
Section: Introductionmentioning
confidence: 99%