Self-Supervised Learning of Dynamic Planar Manipulation of Free-End Cables

TL;DR

This paper introduces a supervised learning method for dynamic manipulation of free-end cables, enabling robots to accurately position cable endpoints outside their immediate reach, with promising results on physical experiments.

Contribution

It presents a novel simulation-based training approach for cable manipulation, closely matching real-world physics, and demonstrates improved accuracy over baseline methods on a UR5 robot.

Findings

Median error distance is 22-35% of cable length.

Outperforms analytic baseline by 21%.

Outperforms Gaussian Process baseline by 7%.

Abstract

Dynamic manipulation of free-end cables has applications for cable management in homes, warehouses and manufacturing plants. We present a supervised learning approach for dynamic manipulation of free-end cables, focusing on the problem of getting the cable endpoint to a designated target position, which may lie outside the reachable workspace of the robot end effector. We present a simulator, tune it to closely match experiments with physical cables, and then collect training data for learning dynamic cable manipulation. We evaluate with 3 cables and a physical UR5 robot. Results over 32x5 trials on 3 cables suggest that a physical UR5 robot can attain a median error distance ranging from 22% to 35% of the cable length among cables, outperforming an analytic baseline by 21% and a Gaussian Process baseline by 7% with lower interquartile range (IQR).