Diffusion-based learning of contact plans for agile locomotion

Understand

Legged robots have become capable of performing highly dynamic maneuvers in the past few years.

• However, agile locomotion in highly constrained environments such as stepping stones is still a challenge.
• In this paper, we propose a combination of model-based control, search, and learning to design efficient control policies for agile locomotion on stepping stones.
• In our framework, we use nonlinear model predictive control (NMPC) to generate whole-body motions for a given contact plan.