RISE: 3D知覚で実世界ロボット模倣をシンプルかつ効果的に
RISE: 3D Perception Makes Real-World Robot Imitation Simple and Effective
単一視点の点群から連続的なロボット動作を直接予測するエンドツーエンドの模倣学習手法を提案し、2D・3Dの既存手法を大きく上回る精度と効率を実現した。
著者: Chenxi Wang, Hongjie Fang, Hao-Shu Fang, Cewu Lu
分類: cs.RO
原文アブストラクト
Precise robot manipulations require rich spatial information in imitation learning. Image-based policies model object positions from fixed cameras, which are sensitive to camera view changes. Policies utilizing 3D point clouds usually predict keyframes rather than continuous actions, posing difficulty in dynamic and contact-rich scenarios. To utilize 3D perception efficiently, we present RISE, an end-to-end baseline for real-world imitation learning, which predicts continuous actions directly from single-view point clouds. It compresses the point cloud to tokens with a sparse 3D encoder. After adding sparse positional encoding, the tokens are featurized using a transformer. Finally, the features are decoded into robot actions by a diffusion head. Trained with 50 demonstrations for each real-world task, RISE surpasses currently representative 2D and 3D policies by a large margin, showcasing significant advantages in both accuracy and efficiency. Experiments also demonstrate that RISE is more general and robust to environmental change compared with previous baselines. Project website: rise-policy.github.io.
