Hierarchical Planning for Rope Manipulation using Knot Theory and a Learned Inverse Model

Matan Sudry, Tom Jurgenson, Aviv Tamar, Erez Karpas

Research output: Contribution to journalConference articlepeer-review

Abstract

This work considers planning the manipulation of deformable 1-dimensional objects such as ropes or cables, specifically to tie knots. We propose TWISTED: Tying With Inverse model and Search in Topological space Excluding Demos, a hierarchical planning approach which, at the high level, uses ideas from knot theory to plan a sequence of rope topological states, while at the low level uses a neural-network inverse model to move between the configurations in the high-level plan. To train the neural network, we propose a self-supervised approach, where we learn from random movements of the rope. To focus the random movements on interesting configurations, such as knots, we propose a non-uniform sampling method tailored for this domain. In a simulation, we show that our approach can plan significantly faster and more accurately than baselines. We also show that our plans are robust to parameter changes in the physical simulation, suggesting future applications via sim2real.

Original languageEnglish
JournalProceedings of Machine Learning Research
Volume229
StatePublished - 2023
Event7th Conference on Robot Learning, CoRL 2023 - Atlanta, United States
Duration: 6 Nov 20239 Nov 2023

Keywords

  • Knot tying
  • Learning
  • Planning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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