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

Matan Sudry, Tom Jurgenson, Aviv Tamar, Erez Karpas

Keywords: Grasping & Manipulation, Robot Learning and Robot Planning

TL;DR: A hierarchical search algorithm that exploits prior knowledge about knot-tying geometry for its high-level plan, with self-supervised learning of an inverse model for executing the low-level control

Abstract: This work considers planning the manipulation of deformable 1-dimensional objects such as ropes or cables, with an emphasis of planning 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 configurations, 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.

Submission Number: 15