クラウド経由で片腕ロボットに実物の衣類を折らせる研究
Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research
クラウドロボティクス基盤Reachを使い、物理ロボット上で布操作アルゴリズムを体系的に比較評価した初の取り組み。片腕でTシャツを折るタスクで、模倣学習と解析手法を組み合わせた新アルゴリズムが人間の84%の性能を達成した。
著者: Ryan Hoque, Kaushik Shivakumar, Shrey Aeron, Gabriel Deza, Aditya Ganapathi, Adrian Wong, Johnny Lee, Andy Zeng, Vincent Vanhoucke, Ken Goldberg
分類: cs.RO, cs.AI
原文アブストラクト
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.
