  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.
