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

• URL: http://arxiv.org/abs/2409.15710v1
• Date: Tue, 24 Sep 2024 03:58:18 GMT
• Title: Autotuning Bipedal Locomotion MPC with GRFM-Net for Efficient Sim-to-Real Transfer
• Authors: Qianzhong Chen, Junheng Li, Sheng Cheng, Naira Hovakimyan, Quan Nguyen,
• Abstract summary: We address the challenges of parameter selection in bipedal locomotion control using DiffTune. A major difficulty lies in balancing model fidelity with differentiability. We validate the parameters learned by DiffTune with GRFM-Net in hardware experiments.
• Score: 10.52309107195141
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We address this difficulty using a low-fidelity model for differentiability, enhanced by a Ground Reaction Force-and-Moment Network (GRFM-Net) to capture discrepancies between MPC commands and actual control effects. We validate the parameters learned by DiffTune with GRFM-Net in hardware experiments, which demonstrates the parameters' optimality in a multi-objective setting compared with baseline parameters, reducing the total loss by up to 40.5$\%$ compared with the expert-tuned parameters. The results confirm the GRFM-Net's effectiveness in mitigating the sim-to-real gap, improving the transferability of simulation-learned parameters to real hardware.

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