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Bipedal Locomotion with Nonlinear Model Predictive Control: Online Gait Generation using Whole-Body Dynamics

Galliker, Manuel Y. and Csomay-Shanklin, Noel and Grandia, Ruben and Taylor, Andrew J. and Farshidian, Farbod and Hutter, Marco and Ames, Aaron D. (2022) Bipedal Locomotion with Nonlinear Model Predictive Control: Online Gait Generation using Whole-Body Dynamics. . (Unpublished)

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The ability to generate dynamic walking in real-time for bipedal robots with compliance and underactuation has the potential to enable locomotion in complex and unstructured environments. Yet, the high-dimensional nature of bipedal robots has limited the use of full-order rigid body dynamics to gaits which are synthesized offline and then tracked online, e.g., via whole-body controllers. In this work we develop an online nonlinear model predictive control approach that leverages the full-order dynamics to realize diverse walking behaviors. Additionally, this approach can be coupled with gaits synthesized offline via a terminal cost that enables a shorter prediction horizon; this makes rapid online re-planning feasible and bridges the gap between online reactive control and offline gait planning. We demonstrate the proposed method on the planar robot AMBER-3M, both in simulation and on hardware.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Csomay-Shanklin, Noel0000-0002-2361-1694
Grandia, Ruben0000-0002-8971-6843
Taylor, Andrew J.0000-0002-5990-590X
Hutter, Marco0000-0001-9049-534X
Ames, Aaron D.0000-0003-0848-3177
Additional Information:This research was supported by NSF NRI award 1924526, NSF award 1932091, NSF CMMI award 1923239, and the Swiss National Science Foundation through the National Centre of Competence in Research Robotics (NCCR Robotics).
Funding AgencyGrant Number
Swiss National Science Foundation (SNSF)UNSPECIFIED
Record Number:CaltechAUTHORS:20220325-224420513
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:114095
Deposited By: George Porter
Deposited On:28 Mar 2022 15:27
Last Modified:28 Mar 2022 15:27

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