FetchMan: シミュレーション経験から学ぶ視覚的ヒューマノイド移動操作ポリシー
FetchMan: Learning Visual Humanoid Loco-Manipulation Policies from Simulated Experiences
シミュレーションで合成デモをクローンするだけでは性能が頭打ちになることを発見し、強化学習とFlow-GRPOによる微調整を組み合わせたエンドツーエンドのsim-to-realパイプラインを構築。実機Unitree G1で未見シーンへのゼロショット到達・把持を73.3%の成功率で達成した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Omar Rayyan, Zhi Li, Max Argus, Yuxin Jiang, Chang Yu, Chenfanfu Jiang, Yuchen Cui
分類: cs.RO
原文アブストラクト
Visual loco-manipulation policies that can generalize to novel scenes and objects have long been a goal of robotics research. However, today's data-hungry algorithms make collecting sufficient demonstrations a struggle for tabletop manipulation, and even more so for humanoids that must also walk and balance. Learning from simulated data and transferring that behavior to the real world, as is commonly done in locomotion, sidesteps this struggle, so we replicate that recipe for loco-manipulation. In doing so, we find that cloning synthetic demonstrations results in a low performance ceiling no matter the amount of training data. Reinforcement learning breaks through it, and refining the cloned policy with Flow-GRPO on a single sparse reward yields performance that synthetic behavior cloning cannot match. Together, these stages form our end-to-end sim-to-real pipeline spanning more than 150,000 scenes, which we use to train FetchMan. We evaluate it on FetchMan-Bench, a simulation benchmark we release, and deploy it zero-shot on a real Unitree G1, where our single-object reach-and-pick policy walks to and grasps a target across unseen scenes at 73.3% success. Finally, we extend this recipe to multi-object training, a first step toward loco-manipulation generalist policies at this data scale.