OASIS: シミュレーションデータ収集から実世界のヒューマノイド移動操作へ
OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation
実世界の画像から3D生成モデルで物体アセットを自動構築し、シミュレーション内で遠隔操作によりデータを収集・拡張して、ヒューマノイドの移動操作ポリシーを訓練する枠組みを提案。実機ゼロショット展開で、多くのタスクで実機遠隔操作データによる訓練を上回る成功率を達成。
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: Zehao Yu, Jiakun Zheng, Weiji Xie, Jiyuan Shi, Chenyun Zhang, Chenjia Bai, Xuelong Li
分類: cs.RO
原文アブストラクト
Recent progress in robot manipulation has been largely driven by learning from large-scale demonstrations. For humanoid robot loco-manipulation tasks, however, existing data sources force an unsatisfying tradeoff between trajectory quality and scalability. Real-world teleoperation provides the highest-quality trajectories but requires dedicated physical space and time-consuming scene resets. Simulation offers an alternative way out of this dilemma: it can produce clean, embodiment-aligned data at scale without any physical hardware. In this paper, we propose OASIS, a simulation-data-driven framework for humanoid loco-manipulation. OASIS automatically reconstructs realistic object assets from real-world images using a 3D generative model. Based on these assets, trajectories are first collected through teleoperation in simulation, and then augmented under diverse domain randomizations in a post-processing stage. With the resulting simulation data, we further design a hierarchical visuomotor policy for humanoid loco-manipulation. Extensive experiments on the real humanoid robot show that, under zero-shot deployment, the policy trained on our simulation data achieves higher success rates on most tasks than that trained on real-robot teleoperation data, owing largely to the broad lighting and environmental variations covered by our simulation rendering, which real-robot data fails to capture. The project page is available at https://oasis-humanoid.github.io/.
関連論文
- VLK: 再構築シーンでの合成インタラクションによるヒューマノイドの移動操作学習ヒューマノイド/移動操作/sim2real
- AGILE：ヒューマノイドの移動操作学習のための包括的ワークフローヒューマノイド/移動操作/sim2real
