Sim-to-Real of Soft Robots with Learned Residual Physics
CoRR(2024)
摘要
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
freedom but can effectively bridge the sim-to-real gap for high dimensional
systems.
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