提出双臂机器人操作新框架，提升空间定位与轨迹灵活性。
Gripper Keypose and Object Pointflow as Interfaces for Bimanual Robotic Manipulation
- 用抓手姿态和物体点流作为中间接口，端到端联合预测动作。
- 仿真与真实场景下性能领先，最高提升27.5%且具强泛化能力。
- 适合需要高精度双臂协同操作的研究者与工业应用开发者。
双臂操作是关键但极具挑战的机器人能力，需精确的空间定位与多样运动轨迹。现有方法分两类：基于关键帧的策略缺乏帧间监督，难以实现连续或弯曲轨迹；连续控制方法则空间感知能力弱。本文提出端到端框架PPI（keyPose and Pointflow Interface），联合预测目标抓手姿态与物体点流，并与连续动作估计结合。该接口使模型聚焦于操作区域，整体框架生成多样化且无碰撞的轨迹。在广泛评估中，PPI显著优于先前方法，在RLBench2仿真基准上提升16.1%，在四个真实任务中平均提升27.5%。其在真实场景中表现稳定、精度高、泛化能力强。
原文摘要 · Abstract (English)
Bimanual manipulation is a challenging yet crucial robotic capability, demanding precise spatial localization and versatile motion trajectories, which pose significant challenges to existing approaches. Existing approaches fall into two categories: keyframe-based strategies, which predict gripper poses in keyframes and execute them via motion planners, and continuous control methods, which estimate actions sequentially at each timestep. The keyframe-based method lacks inter-frame supervision, struggling to perform consistently or execute curved motions, while the continuous method suffers from weaker spatial perception. To address these issues, this paper introduces an end-to-end framework PPI (keyPose and Pointflow Interface), which integrates the prediction of target gripper poses and object pointflow with the continuous actions estimation. These interfaces enable the model to effectively attend to the target manipulation area, while the overall framework guides diverse and collision-free trajectories. By combining interface predictions with continuous actions estimation, PPI demonstrates superior performance in diverse bimanual manipulation tasks, providing enhanced spatial localization and satisfying flexibility in handling movement restrictions. In extensive evaluations, PPI significantly outperforms prior methods in both simulated and real-world experiments, achieving state-of-the-art performance with a +16.1% improvement on the RLBench2 simulation benchmark and an average of +27.5% gain across four challenging real-world tasks. Notably, PPI exhibits strong stability, high precision, and remarkable generalization capabilities in real-world scenarios. Project page: https://yuyinyang3y.github.io/PPI/
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