用磁性触觉皮肤提升机器人精准抓取能力
Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins
- 用磁性触觉皮肤+视觉融合的Transformer策略
- 四类真实任务平均性能提升27.5%
- 适合需要高精度接触操作的机器人场景
尽管视觉运动策略已推动机器人操作发展，但因视觉难以推理物理交互，执行高接触任务仍具挑战。现有方法多依赖光学触觉传感器，或仅限识别任务，或需复杂降维。本文探索使用磁性皮肤传感器——其天然低维、高灵敏且易于集成。提出Visuo-Skin（ViSk）框架，将皮肤数据作为额外标记与视觉信息一同输入Transformer策略。在信用卡刷卡、插头插入、USB插入和书架取物四项复杂真实任务中，ViSk显著优于纯视觉及光学触觉策略。分析显示，融合触觉与视觉可提升性能与空间泛化能力，平均提升27.5%。
原文摘要 · Abstract (English)
While visuomotor policy learning has advanced robotic manipulation, precisely executing contact-rich tasks remains challenging due to the limitations of vision in reasoning about physical interactions. To address this, recent work has sought to integrate tactile sensing into policy learning. However, many existing approaches rely on optical tactile sensors that are either restricted to recognition tasks or require complex dimensionality reduction steps for policy learning. In this work, we explore learning policies with magnetic skin sensors, which are inherently low-dimensional, highly sensitive, and inexpensive to integrate with robotic platforms. To leverage these sensors effectively, we present the Visuo-Skin (ViSk) framework, a simple approach that uses a transformer-based policy and treats skin sensor data as additional tokens alongside visual information. Evaluated on four complex real-world tasks involving credit card swiping, plug insertion, USB insertion, and bookshelf retrieval, ViSk significantly outperforms both vision-only and optical tactile sensing based policies. Further analysis reveals that combining tactile and visual modalities enhances policy performance and spatial generalization, achieving an average improvement of 27.5% across tasks. https://visuoskin.github.io/
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