用自监督学习让机械手触觉皮肤更懂抓物动作
Self-supervised perception for tactile skin covered dexterous hands
- 通过自蒸馏训练，从无标签抓取数据中学习触觉表征
- 在多个任务中提升性能超41%，样本效率显著
- 适合需要全手触觉感知的机器人灵巧操作研究
我们提出Sparsh-skin，一种针对分布在灵巧机器人手部指尖、指节和掌面的磁性触觉皮肤的预训练编码器。相比仅覆盖指尖且受带宽限制的视觉触觉传感器，磁性触觉皮肤具有灵活形态和快速响应优势，能实现全手覆盖感知，对机器人灵巧操作至关重要。然而通用模型缺乏、磁通量解读困难及校准问题限制了其应用。Sparsh-skin利用带有Xela uSkin的Allegro机械手采集的大量未标注手物交互数据，通过自蒸馏进行自监督训练，输出可用于任意下游任务的潜在触觉嵌入。在状态估计、策略学习等多个基准任务中，预训练的Sparsh-skin表征在样本效率上表现优异，任务性能相较之前工作提升超过41%，相比端到端学习提升超过56%。
原文摘要 · Abstract (English)
We present Sparsh-skin, a pre-trained encoder for magnetic skin sensors distributed across the fingertips, phalanges, and palm of a dexterous robot hand. Magnetic tactile skins offer a flexible form factor for hand-wide coverage with fast response times, in contrast to vision-based tactile sensors that are restricted to the fingertips and limited by bandwidth. Full hand tactile perception is crucial for robot dexterity. However, a lack of general-purpose models, challenges with interpreting magnetic flux and calibration have limited the adoption of these sensors. Sparsh-skin, given a history of kinematic and tactile sensing across a hand, outputs a latent tactile embedding that can be used in any downstream task. The encoder is self-supervised via self-distillation on a variety of unlabeled hand-object interactions using an Allegro hand sensorized with Xela uSkin. In experiments across several benchmark tasks, from state estimation to policy learning, we find that pretrained Sparsh-skin representations are both sample efficient in learning downstream tasks and improve task performance by over 41% compared to prior work and over 56% compared to end-to-end learning.
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