Feedback MPC for Torque-Controlled Legged Robots
This work addresses the challenge of achieving real-time torque control for legged robots, which is crucial for dynamic locomotion in robotics, though it appears incremental as it builds on existing MPC and DDP methods with specific enhancements.
The paper tackled the problem of insufficient computational power for torque-level whole-body Model Predictive Control (MPC) at high update rates in legged robots by using a feedback policy from a Differential Dynamic Programming (DDP)-based MPC algorithm to bridge the gap between low MPC updates and actuation rates, demonstrating stable locomotion policies for the ANYmal quadruped in simulation and on hardware.
The computational power of mobile robots is currently insufficient to achieve torque level whole-body Model Predictive Control (MPC) at the update rates required for complex dynamic systems such as legged robots. This problem is commonly circumvented by using a fast tracking controller to compensate for model errors between updates. In this work, we show that the feedback policy from a Differential Dynamic Programming (DDP) based MPC algorithm is a viable alternative to bridge the gap between the low MPC update rate and the actuation command rate. We propose to augment the DDP approach with a relaxed barrier function to address inequality constraints arising from the friction cone. A frequency-dependent cost function is used to reduce the sensitivity to high-frequency model errors and actuator bandwidth limits. We demonstrate that our approach can find stable locomotion policies for the torque-controlled quadruped, ANYmal, both in simulation and on hardware.