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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
Andy Zeng
Ken Goldberg
IEEE International Conference on Intelligent Robots and Systems (IROS) (2022) (to appear)


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 new 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.