Autotuning Bipedal Locomotion MPC with GRFM-Net for Efficient Sim-to-Real Transfer

Understand

Bipedal locomotion control is essential for humanoid robots to navigate complex, human-centric environments.

• While optimization-based control designs are popular for integrating sophisticated models of humanoid robots, they often require labor-intensive manual tuning.
• In this work, we address the challenges of parameter selection in bipedal locomotion control using DiffTune, a model-based autotuning method that leverages differential programming for efficient parameter learning.
• A major difficulty lies in balancing model fidelity with differentiability.