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arXiv · 2405.09581

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

Jonathan Wang·Huang Huang·Vincent Lim·Harry Zhang·Jeffrey Ichnowski·Daniel Seita·Yunliang Chen·Ken Goldberg
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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).

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Jonathan Wang, Huang Huang, Vincent Lim, Harry Zhang, Jeffrey Ichnowski, Daniel Seita, Yunliang Chen, Ken Goldberg. 2024-05-15. Self-Supervised Learning of Dynamic Planar Manipulation of Free-End Cables. https://arxiv.org/abs/2405.09581

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