Simultaneous Learning of Contact and Continuous Dynamics

Bibit Bianchini, Mathew Halm, and Michael Posa

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.

@inproceedings{Bianchini2023,
  title = {Simultaneous Learning of Contact and Continuous Dynamics},
  author = {Bianchini, Bibit and Halm, Mathew and Posa, Michael},
  year = {2023},
  month = nov,
  booktitle = {Conference on Robot Learning (CoRL)},
  url = {https://proceedings.mlr.press/v229/bianchini23a.html},
  website = {https://sites.google.com/view/continuous-contact-nets/home},
  code = {https://github.com/ebianchi/dair_pll},
  youtube = {uMCLCIzbgJo}
}