让机器人动作更自然：通过空间对齐提升抓取成功率
OASIS: Observation-Action Space Alignment via SE(3) Trajectory Prediction for Robotic Manipulation
- 用SE(3)轨迹预测实现视觉动作空间对齐
- 仿真与真实场景中成功率显著优于基线模型
- 适合做机器人抓取与泛化任务的研究者参考
近期的视觉-语言-动作（VLA）模型和世界动作模型（WAMs）通过引入辅助空间特征或未来视觉状态预测，提升了机器人操作能力。然而，这些表征仍主要停留在观测空间，未共享动作空间的刚体几何特性，导致动作解码器需隐式恢复该几何结构。我们提出OASIS，一种通过SE(3)末端执行器轨迹预测实现中间表征与动作空间对齐的视觉运动策略。OASIS结合3D感知特征编码器（融合视觉-语言与度量深度特征）与SE(3)轨迹预测器，生成相机坐标系下的末端执行器轨迹。在预测器的姿态监督隐藏状态条件下，动作解码器生成符合刚体运动规律的动作片段。在仿真与真实世界实验中，OASIS在成功率与分布外泛化能力上均超越VLA和WAM基线模型。
原文摘要 · Abstract (English)
Recent vision-language-action (VLA) models and world action models (WAMs) advance robotic manipulation by enriching intermediate representations with auxiliary spatial features or future visual-state prediction. However, these representations largely remain within the observation space and do not share the rigid-body geometry of the action space, forcing the action decoder to implicitly recover this geometry. We propose OASIS, a visuomotor policy that aligns the intermediate representation with the action space via $SE(3)$ end-effector trajectory prediction. OASIS couples a 3D-aware feature encoder that fuses vision-language and metric-depth features with an $SE(3)$ trajectory predictor that produces a camera-frame end-effector trajectory. Conditioned on the predictor's pose-supervised hidden states, the action decoder generates action chunks consistent with rigid-body motion. Across simulation and real-world experiments, OASIS outperforms VLA and WAM baselines in success rate and out-of-distribution generalization. Our project page is available at https://npuhandsome.github.io/OASIS_web.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。