  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.

Robotics