用双手触觉主动探索，无需视觉也能精准估计物体姿态。
Object Pose Estimation through Dexterous Touch
- 双臂协作：一手固定物体，另一手主动触探采集数据
- 通过强化学习收集触觉点云，迭代优化物体形状与姿态
- 无需先验几何知识，适合光照差或遮挡场景
在视觉受限或受光照、遮挡、外观变化影响的场景中，鲁棒的物体姿态估计对机器人操作与交互至关重要。触觉传感器通常只能提供局部接触信息，难以从部分数据重建完整姿态。本文提出一种基于传感运动探索的方法，通过强化学习控制机械手主动与物体交互，采集触觉数据。利用采集到的3D点云，迭代优化物体的形状与姿态。实验设置中，一手持握物体保持稳定，另一手执行主动探索。结果表明，该方法可无需先验几何知识，主动探测物体表面以识别关键姿态特征。补充材料及更多演示详见 https://amirshahid.github.io/BimanualTactilePose。
原文摘要 · Abstract (English)
Robust object pose estimation is essential for manipulation and interaction tasks in robotics, particularly in scenarios where visual data is limited or sensitive to lighting, occlusions, and appearances. Tactile sensors often offer limited and local contact information, making it challenging to reconstruct the pose from partial data. Our approach uses sensorimotor exploration to actively control a robot hand to interact with the object. We train with Reinforcement Learning (RL) to explore and collect tactile data. The collected 3D point clouds are used to iteratively refine the object's shape and pose. In our setup, one hand holds the object steady while the other performs active exploration. We show that our method can actively explore an object's surface to identify critical pose features without prior knowledge of the object's geometry. Supplementary material and more demonstrations will be provided at https://amirshahid.github.io/BimanualTactilePose .
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