Simultaneous Learning of Contact and Continuous Dynamics

November 2023   Bianchini

Project Documents

We published our work on data efficient model building through contact in a paper at the Conference on Robot Learning (CoRL) 2023 in Atlanta, GA. My co-authors are recent Penn PhD graduate Mathew Halm and our advisor, Professor Michael Posa. Our project simultaneously built contact and continuous dynamics of novel, possibly multi-link objects by observing their dynamics through collision-rife trajectory data.

We've made all of the below publicly available for any interested researchers/readers:

• Paper:  in Proceedings of Machine Learning Research.
• Code:  Github fork of dair_pll repository with code for training our Continuous + ContactNets (CCN) method and DiffSim/End-to-end alternatives.
• Dataset:  google drive link with over 500 tosses of a novel articulated object undergoing contact-rich trajectories.
• Project page:  google sites page made for anonymous review (this page contains all the same information).

Video 1:  My 1-minute spotlight talk, submitted and presented live at CoRL 2023.

Paper 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.

Citation

@inproceedings{bianchini2023simultaneous,
  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://openreview.net/forum?id=-3G6_D66Aua}
}

Dataset