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Robust Predictive Control for Quadrupedal Locomotion: Learning to Close the Gap Between Reduced- and Full-Order Models

Pandala, Abhishek and Fawcett, Randall T. and Rosolia, Ugo and Ames, Aaron D. and Hamed, Kaveh Akbari (2022) Robust Predictive Control for Quadrupedal Locomotion: Learning to Close the Gap Between Reduced- and Full-Order Models. IEEE Robotics and Automation Letters, 7 (3). pp. 6622-6629. ISSN 2377-3766. doi:10.1109/lra.2022.3176105.

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Template-based reduced-order models have provided a popular methodology for real-time trajectory planning of dynamic quadrupedal locomotion. However, the abstraction and unmodeled dynamics in template models significantly increase the gap between reduced- and full-order models. This letter presents a computationally tractable robust model predictive control (RMPC) formulation, based on convex quadratic programs (QP), to bridge this gap. The RMPC framework considers the single rigid body model subject to a set of unmodeled dynamics and plans for the optimal reduced-order trajectory and ground reaction forces (GRFs). The generated optimal GRFs of the high-level RMPC are then mapped to the full-order model using a low-level nonlinear controller based on virtual constraints and QP. The proposed hierarchical control framework is employed for locomotion over rough terrains. We leverage deep reinforcement learning to train a neural network to compute the set of unmodeled dynamics for the RMPC framework. The proposed controller is finally validated via extensive numerical simulations and experiments for robust and blind locomotion of the A1 quadrupedal robot on different terrains.

Item Type:Article
Related URLs:
URLURL TypeDescription
Pandala, Abhishek0000-0002-7424-5508
Fawcett, Randall T.0000-0002-7704-2706
Rosolia, Ugo0000-0002-1682-0551
Ames, Aaron D.0000-0003-0848-3177
Hamed, Kaveh Akbari0000-0001-9597-1691
Additional Information:© 2022 IEEE. Manuscript received January 11, 2022; accepted May 6, 2022. Date of publication May 20, 2022; date of current version May 26, 2022. This letter was recommended for publication by Associate Editor P. M. Wensing and Editor A. Kheddar upon evaluation of the Reviewers’ comments. The work of Abhishek Pandala and Kaveh Akbari Hamed was supported by the National Science Foundation (NSF) under Grant 1923216. The work of Randall T. Fawcett was supported by the NSF under Grant 2128948. The work of Aaron D. Ames was supported by the NSF under Grant 1923239.
Funding AgencyGrant Number
Subject Keywords:Legged robots, motion control, multi-contact whole-body motion planning and control
Issue or Number:3
Record Number:CaltechAUTHORS:20220715-535824900
Persistent URL:
Official Citation:A. Pandala, R. T. Fawcett, U. Rosolia, A. D. Ames and K. A. Hamed, "Robust Predictive Control for Quadrupedal Locomotion: Learning to Close the Gap Between Reduced- and Full-Order Models," in IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6622-6629, July 2022, doi: 10.1109/LRA.2022.3176105
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115637
Deposited By: Tony Diaz
Deposited On:20 Jul 2022 16:51
Last Modified:20 Jul 2022 16:51

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