<div class="csl-bib-body">
<div class="csl-entry">Egle, T., Yan, Y., Lee, D., & Ott, C. (2024). Enhancing Model-Based Step Adaptation for Push Recovery through Reinforcement Learning of Step Timing and Region. In <i>2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids)</i> (pp. 165–172). IEEE. https://doi.org/10.34726/8160</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/208344
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dc.identifier.uri
https://doi.org/10.34726/8160
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dc.description.abstract
This paper introduces a new approach to enhance the robustness of humanoid walking under strong perturbations, such as substantial pushes. Effective recovery from external disturbances requires bipedal robots to dynamically adjust their stepping strategies, including footstep positions and timing. Unlike most advanced walking controllers that restrict footstep locations to a predefined convex region, substantially limiting recoverable disturbances, our method leverages reinforcement learning to dynamically adjust the permissible footstep region, expanding it to a larger, effectively non-convex area and allowing cross-over stepping, which is crucial for counteracting large lateral pushes. Additionally, our method adapts footstep timing in real time to further extend the range of recoverable disturbances. Based on these adjustments, feasible footstep positions and DCM trajectory are planned by solving a QP. Finally, we employ a DCM controller and an inverse dynamics whole-body control framework to ensure the robot effectively follows the trajectory.
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dc.description.sponsorship
European Commission
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Legged Locomotion
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dc.subject
Humanoid robots
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dc.subject
Reinforcement learning
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dc.subject
Push Recovery
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dc.title
Enhancing Model-Based Step Adaptation for Push Recovery through Reinforcement Learning of Step Timing and Region