Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety

Noel Csomay-Shanklin, Ryan K. Cosner, Min Dai, Andrew J. Taylor, Aaron D. Ames
Proceedings of the 3rd Conference on Learning for Dynamics and Control, PMLR 144:1041-1053, 2021.

Abstract

This paper combines episodic learning and control barrier functions (CBFs) in the setting of bipedal locomotion. The safety guarantees that CBFs provide are only valid with perfect model knowledge; however, this assumption cannot be met on hardware platforms. To address this, we utilize the notion of Projection-to-State safety paired with a machine learning framework in an attempt to learn the model uncertainty as it effects the barrier functions. The proposed approach is demonstrated both in simulation and on hardware for the AMBER-3M bipedal robot in the context of the stepping-stone problem which requires precise foot placement while walking dynamically.

Cite this Paper

BibTeX
@InProceedings{pmlr-v144-csomay-shanklin21a, title = {Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety}, author = {Csomay-Shanklin, Noel and Cosner, Ryan K. and Dai, Min and Taylor, Andrew J. and Ames, Aaron D.}, booktitle = {Proceedings of the 3rd Conference on Learning for Dynamics and Control}, pages = {1041--1053}, year = {2021}, editor = {Jadbabaie, Ali and Lygeros, John and Pappas, George J. and A. Parrilo, Pablo and Recht, Benjamin and Tomlin, Claire J. and Zeilinger, Melanie N.}, volume = {144}, series = {Proceedings of Machine Learning Research}, month = {07 -- 08 June}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v144/csomay-shanklin21a/csomay-shanklin21a.pdf}, url = {https://proceedings.mlr.press/v144/csomay-shanklin21a.html}, abstract = {This paper combines episodic learning and control barrier functions (CBFs) in the setting of bipedal locomotion. The safety guarantees that CBFs provide are only valid with perfect model knowledge; however, this assumption cannot be met on hardware platforms. To address this, we utilize the notion of Projection-to-State safety paired with a machine learning framework in an attempt to learn the model uncertainty as it effects the barrier functions. The proposed approach is demonstrated both in simulation and on hardware for the AMBER-3M bipedal robot in the context of the stepping-stone problem which requires precise foot placement while walking dynamically.} }
Endnote
%0 Conference Paper %T Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety %A Noel Csomay-Shanklin %A Ryan K. Cosner %A Min Dai %A Andrew J. Taylor %A Aaron D. Ames %B Proceedings of the 3rd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2021 %E Ali Jadbabaie %E John Lygeros %E George J. Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire J. Tomlin %E Melanie N. Zeilinger %F pmlr-v144-csomay-shanklin21a %I PMLR %P 1041--1053 %U https://proceedings.mlr.press/v144/csomay-shanklin21a.html %V 144 %X This paper combines episodic learning and control barrier functions (CBFs) in the setting of bipedal locomotion. The safety guarantees that CBFs provide are only valid with perfect model knowledge; however, this assumption cannot be met on hardware platforms. To address this, we utilize the notion of Projection-to-State safety paired with a machine learning framework in an attempt to learn the model uncertainty as it effects the barrier functions. The proposed approach is demonstrated both in simulation and on hardware for the AMBER-3M bipedal robot in the context of the stepping-stone problem which requires precise foot placement while walking dynamically.
APA
Csomay-Shanklin, N., Cosner, R.K., Dai, M., Taylor, A.J. & Ames, A.D.. (2021). Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety. Proceedings of the 3rd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research 144:1041-1053 Available from https://proceedings.mlr.press/v144/csomay-shanklin21a.html.

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