Impact Invariant Control with Applications to Bipedal Locomotion

TL;DR

This paper introduces a control framework that maintains robustness during impacts in legged robots by projecting control objectives onto impact-invariant subspaces, enhancing stability and performance in dynamic locomotion.

Contribution

It proposes a novel impact-invariant control projection method that improves robustness to impact uncertainties in legged robot locomotion.

Findings

Effective in simulation and hardware tests

Improves robustness during impact events

Applicable to various bipedal robot controllers

Abstract

When legged robots impact their environment, they undergo large changes in their velocities in a small amount of time. Measuring and applying feedback to these velocities is challenging, and is further complicated due to 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 utility of the projection on a walking controller for a planar five-link-biped and on a jumping controller for a compliant 3D bipedal robot, Cassie. The effectiveness of our method is shown to translate well on hardware.