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arXiv · 2204.10297

Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research

Ryan Hoque·Kaushik Shivakumar·Shrey Aeron·Gabriel Deza·Aditya Ganapathi·Adrian Wong·Johnny Lee·Andy Zeng·Vincent Vanhoucke·Ken Goldberg
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Abstract

Autonomous fabric manipulation is a longstanding challenge in robotics, but evaluating progress is difficult due to the cost and diversity of robot hardware. Using Reach, a cloud robotics platform that enables low-latency remote execution of control policies on physical robots, we present the first systematic benchmarking of fabric manipulation algorithms on physical hardware. We develop 4 novel learning-based algorithms that model expert actions, keypoints, reward functions, and dynamic motions, and we compare these against 4 learning-free and inverse dynamics algorithms on the task of folding a crumpled T-shirt with a single robot arm. The entire lifecycle of data collection, model training, and policy evaluation is performed remotely without physical access to the robot workcell. Results suggest a new algorithm combining imitation learning with analytic methods achieves 84% of human-level performance on the folding task. See https://sites.google.com/berkeley.edu/cloudfolding for all data, code, models, and supplemental material.

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Ryan Hoque, Kaushik Shivakumar, Shrey Aeron, Gabriel Deza, Aditya Ganapathi, Adrian Wong, Johnny Lee, Andy Zeng, Vincent Vanhoucke, Ken Goldberg. 2022-04-21. Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research. https://arxiv.org/abs/2204.10297

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