Sim-to-Real of Soft Robots with Learned Residual Physics
<|reference_start|>Sim-to-Real of Soft Robots with Learned Residual Physics: Accurately modeling soft robots in simulation is computationally expensive and commonly falls short of representing the real world. This well-known discrepancy, known as the sim-to-real gap, can have several causes, such as coarsely approximated geometry and material models, manufacturing defects, viscoelasticity and plasticity, and hysteresis effects. Residual physics networks learn from real-world data to augment a discrepant model and bring it closer to reality. Here, we present a residual physics method for modeling soft robots with large degrees of freedom. We train neural networks to learn a residual term -- the modeling error between simulated and physical systems. Concretely, the residual term is a force applied on the whole simulated mesh, while real position data is collected with only sparse motion markers. The physical prior of the analytical simulation provides a starting point for the residual network, and the combined model is more informed than if physics were learned tabula rasa. We demonstrate our method on 1) a silicone elastomeric beam and 2) a soft pneumatic arm with hard-to-model, anisotropic fiber reinforcements. Our method outperforms traditional system identification up to 60%. We show that residual physics need not be limited to low degrees of freedom but can effectively bridge the sim-to-real gap for high dimensional systems.<|reference_end|>
@article{gao2024sim-to-real,
    title={Sim-to-Real of Soft Robots with Learned Residual Physics},
    author={Junpeng Gao, Mike Yan Michelis, Andrew Spielberg, Robert K.
  Katzschmann},
    journal={IEEE Robotics and Automation Letters (Volume: 9, Issue: 10,
  October 2024)},
    year={2024},
    doi={10.1109/LRA.2024.3446287},
    archivePrefix={arXiv},
    eprint={2402.01086},
    primaryClass={cs.RO}
}