Autotuning Bipedal Locomotion MPC with GRFM-Net for Efficient Sim-to-Real Transfer
<|reference_start|>Autotuning Bipedal Locomotion MPC with GRFM-Net for Efficient Sim-to-Real Transfer: 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.<|reference_end|>
@article{chen2024autotuning,
    title={Autotuning Bipedal Locomotion MPC with GRFM-Net for Efficient
  Sim-to-Real Transfer},
    author={Qianzhong Chen, Junheng Li, Sheng Cheng, Naira Hovakimyan, Quan Nguyen},
    journal={arXiv preprint arXiv:2409.15710},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15710},
    primaryClass={cs.RO cs.AI cs.SY eess.SY}
}