Autonomously Untangling Long Cables

Publication info

Vainavi Viswanath*, Kaushik Shivakumar*, Justin Kerr*, Brijen Thananjeyan, Ellen Novoseller, Jeffrey Ichnowski, Alejandro Escontrela, Michael Laskey, Joseph E. Gonzalez, and Ken Goldberg

Robotics Proceedings 2022

Best Systems Paper Award,
(1.6%)

Bib PDF Website

@article{Viswanath22RSS_SGTM,
  title = {Autonomously Untangling Long Cables},
  author = {Viswanath, Vainavi and Shivakumar, Kaushik and Kerr, Justin and Thananjeyan, Brijen and Novoseller, Ellen and Ichnowski, Jeffrey and Escontrela, Alejandro and Laskey, Michael and Gonzalez, Joseph E. and Goldberg, Ken},
  journal = {Robotics Proceedings},
  year = {2022},
  url = {https://roboticsconference.org/2022/program/papers/034/},
}

Sliding and Grasping for Tangle Manipulation (SGTM)

The following video briefly summarizes the SGTM algorithm. Our RSS spotlight provides a more detailed explanation of the algorithm, and discusses interesting experimental evaluations.

Knot Detection

RSS 2021 Spotlight

Acknowledgements

This research was performed at the AUTOLAB at UC Berkeley in affiliation with the Berkeley AI Research (BAIR) Lab, the CITRIS “People and Robots” (CPAR) Initiative, and the RealTime Intelligent Secure Execution (RISE) Lab. The authors were supported in part by donations from Toyota Research Institute and by equipment grants from PhotoNeo, Nvidia, and Intuitive Surgical.

How to cite

@article{Viswanath22RSS_SGTM,
  title = {Autonomously Untangling Long Cables},
  author = {Viswanath, Vainavi and Shivakumar, Kaushik and Kerr, Justin and Thananjeyan, Brijen and Novoseller, Ellen and Ichnowski, Jeffrey and Escontrela, Alejandro and Laskey, Michael and Gonzalez, Joseph E. and Goldberg, Ken},
  journal = {Robotics Proceedings},
  year = {2022},
  url = {https://roboticsconference.org/2022/program/papers/034/},
}

Paper