Online planning of whole-body motions for legged robots is challenging due to the inherent
nonlinearity in the robot dynamics. In this work, we propose a nonlinear model predictive …

Legged locomotion holds the premise of universal mobility, a critical capability for many real-
world robotic applications. Both model-based and learning-based approaches have …

Nonlinear model predictive control (NMPC) is one the most powerful tools for generating
control policies for legged locomotion. However, the large computation load required for …

In Receding Horizon Planning (RHP), it is critical that the motion being executed facilitates
the completion of the task, eg building momentum to overcome large obstacles. This …

Locomotion is realized through making and breaking contact. State-of-the-art constrained
nonlinear model predictive controllers (NMPC) generate whole-body trajectories for a given …

Safe learning of locomotion skills is still an open problem. Indeed, the intrinsically unstable
nature of the open-loop dynamics of locomotion systems renders naive learning from scratch …

Planning multicontact motions in a receding horizon fashion requires a value function to
guide the planning with respect to the future, eg, building momentum to traverse large …

Deep reinforcement learning uses simulators as abstract oracles to interact with the
environment. In continuous domains of multi-body robotic systems, differentiable simulators …

Over the past years, the autonomy of robots has increased, and first legged locomotion
robots have become commercially available. To further increase the autonomy of these …

Deep reinforcement learning uses simulators as abstract oracles to interact with the
environment. In continuous domains of multi body robotic systems, differentiable simulators …