空间锚定触觉感知用于鲁棒的灵巧操作
Spatially anchored Tactile Awareness for Robust Dexterous Manipulation
AI总结：
SaTA通过空间锚定触觉感知提升灵巧操作的精度和鲁棒性，有效解决亚毫米级任务中的触觉与运动学结合问题。
摘要：
灵巧操作需要精确的几何推理，但现有的视觉-触觉学习方法在亚毫米精度任务上表现不佳，而传统基于模型的方法能够轻松处理此类任务。我们发现一个关键限制：虽然触觉传感器提供丰富的接触信息，但当前的学习框架未能有效利用触觉信号的感知丰富性和其与手部运动学的关系。我们认为理想的触觉表示应将接触测量明确地锚定在稳定的参考系中，同时保留详细的感官信息，使策略能够不仅检测接触发生，还能在手的坐标系中精确推断物体几何形状。我们引入SaTA（空间锚定触觉感知用于灵巧操作），一种端到端的策略框架，通过前向运动学将触觉特征锚定在手部运动学框架中，从而在不需要物体模型或显式姿态估计的情况下实现准确的几何推理。我们的核心见解是，空间锚定的触觉表示使策略不仅能检测接触发生，还能在手的坐标系中精确推断物体几何形状。我们验证了SaTA在具有挑战性的灵巧操作任务上的有效性，包括在自由空间中双臂USB-C连接、需要亚毫米对齐精度的任务，以及需要精确螺纹啮合和旋转控制的灯泡安装任务，以及需要精细力调节和角度精度的卡片滑动任务。这些任务对基于学习的方法来说都是重大挑战，因为它们对精度要求极为严格。在多个基准测试中，SaTA显著优于强大的视觉-触觉基线方法，成功率达到最高提升30个百分点，同时将任务完成时间减少了27个百分点。
英文摘要：
Dexterous manipulation requires precise geometric reasoning, yet existing visuo-tactile learning methods struggle with sub-millimeter precision tasks that are routine for traditional model-based approaches. We identify a key limitation: while tactile sensors provide rich contact information, current learning frameworks fail to effectively leverage both the perceptual richness of tactile signals and their spatial relationship with hand kinematics. We believe an ideal tactile representation should explicitly ground contact measurements in a stable reference frame while preserving detailed sensory information, enabling policies to not only detect contact occurrence but also precisely infer object geometry in the hand's coordinate system. We introduce SaTA (Spatially-anchored Tactile Awareness for dexterous manipulation), an end-to-end policy framework that explicitly anchors tactile features to the hand's kinematic frame through forward kinematics, enabling accurate geometric reasoning without requiring object models or explicit pose estimation. Our key insight is that spatially grounded tactile representations allow policies to not only detect contact occurrence but also precisely infer object geometry in the hand's coordinate system. We validate SaTA on challenging dexterous manipulation tasks, including bimanual USB-C mating in free space, a task demanding sub-millimeter alignment precision, as well as light bulb installation requiring precise thread engagement and rotational control, and card sliding that demands delicate force modulation and angular precision. These tasks represent significant challenges for learning-based methods due to their stringent precision requirements. Across multiple benchmarks, SaTA significantly outperforms strong visuo-tactile baselines, improving success rates by up to 30 percentage while reducing task completion times by 27 percentage.
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