  Planning and execution of agile locomotion maneuvers have been a longstanding
challenge in legged robotics. It requires to derive motion plans and local
feedback policies in real-time to handle the nonholonomy of the kinetic
momenta. To achieve so, we propose a hybrid predictive controller that
considers the robot's actuation limits and full-body dynamics. It combines the
feedback policies with tactile information to locally predict future actions.
It converges within a few milliseconds thanks to a feasibility-driven approach.
Our predictive controller enables ANYmal robots to generate agile maneuvers in
realistic scenarios. A crucial element is to track the local feedback policies
as, in contrast to whole-body control, they achieve the desired angular
momentum. To the best of our knowledge, our predictive controller is the first
to handle actuation limits, generate agile locomotion maneuvers, and execute
optimal feedback policies for low level torque control without the use of a
separate whole-body controller.
