Impact Invariant Control with Applications to Bipedal Locomotion
This work addresses control robustness for bipedal locomotion under impact uncertainties, representing an incremental improvement in domain-specific robotics.
The paper tackles the challenge of controlling legged robots during impacts by proposing a framework that projects control objectives to an impact-invariant subspace, resulting in robust performance despite uncertainties. It demonstrates effectiveness on a planar biped and Cassie robot, with successful hardware translation.
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.