When legged robots impact their environment, they undergo large changes in their velocities in a short amount of time. Measuring and applying feedback to these velocities is challenging, further complicated by uncertainty in the impact model and impact timing. This work proposes a general framework for adapting feedback control during impact by projecting the control objectives to a subspace that is invariant to the impact event. The resultant controller is robust to uncertainties in the impact event while maintaining maximum control authority over the impact-invariant subspace. We demonstrate the improved performance of the projection over other commonly used heuristics on a walking controller for a planar five-link-biped. The projection is also applied to jumping, box jumping on to a platform 0.4 m tall, and running controllers for the compliant 3D bipedal robot, Cassie. The modification is easily applied to these various controllers and is a critical component to deploying on the physical robot.
PDF@article{Yang2023,
title = {Impact-Invariant Control: Maximizing Control Authority During Impacts},
author = {Yang, William and Posa, Michael},
year = {2023},
month = mar,
journal = {arXiv preprint arXiv:2303.00817},
arxiv = {2303.00817},
youtube = {_v_CKU47znQ}
}