  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.
