RL-augmented MPC Framework for Agile and Robust Bipedal Footstep Locomotion Planning and Control

Understand

This paper proposes an online bipedal footstep planning strategy that combines model predictive control (MPC) and reinforcement learning (RL) to achieve agile and robust bipedal maneuvers.

• While MPC-based foot placement controllers have demonstrated their effectiveness in achieving dynamic locomotion, their performance is often limited by the use of simplified models and assumptions.
• To address this challenge, we develop a novel foot placement controller that leverages a learned policy to bridge the gap between the use of a simplified model and the more complex full-order robot system.
• Specifically, our approach employs a unique combination of an ALIP-based MPC foot placement controller for sub-optimal footstep planning and the learned policy for refining footstep adjustments, enabling the resulting footstep policy to capture the robot's whole-body dynamics effectively.