Simultaneous Learning of Contact and Continuous DynamicsDownload PDF

Published: 30 Aug 2023, Last Modified: 18 Oct 2023CoRL 2023 PosterReaders: Everyone
Keywords: system identification, dynamics learning, contact-rich manipulation
TL;DR: We simultaneously learn contact and continuous dynamics of novel objects through contact-rich trajectories, using model-based structure and residual physics.
Abstract: Robotic manipulation can greatly benefit from the data efficiency, robustness, and predictability of model-based methods if robots can quickly generate models of novel objects they encounter. This is especially difficult when effects like complex joint friction lack clear first-principles models and are usually ignored by physics simulators. Further, numerically-stiff contact dynamics can make common model-building approaches struggle. We propose a method to simultaneously learn contact and continuous dynamics of a novel, possibly multi-link object by observing its motion through contact-rich trajectories. We formulate a system identification process with a loss that infers unmeasured contact forces, penalizing their violation of physical constraints and laws of motion given current model parameters. Our loss is unlike prediction-based losses used in differentiable simulation. Using a new dataset of real articulated object trajectories and an existing cube toss dataset, our method outperforms differentiable simulation and end-to-end alternatives with more data efficiency. See our project page for code, datasets, and media:
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