面向覆有触觉皮肤的灵巧手的自监督感知
Self-supervised perception for tactile skin covered dexterous hands
AI总结：
针对磁性触觉皮肤传感器缺乏通用模型等问题，研究提出预训练编码器Sparsh-skin，通过自蒸馏自监督学习生成触觉嵌入，在多项基准任务中显著提升样本效率与任务性能。
摘要：
我们提出Sparsh-skin，这是一种预训练编码器，用于分布在灵巧机器人手的指尖、指骨和手掌上的磁性皮肤传感器。与局限于指尖且受带宽限制的基于视觉的触觉传感器相比，磁性触觉皮肤为整手覆盖提供了灵活的外形因素，且响应速度快。全手触觉感知对机器人灵巧性至关重要。然而，通用模型的缺失、磁通量解读与校准方面的难题限制了这类传感器的应用。Sparsh-skin接收整手的运动学与触觉感知历史序列，输出可用于任意下游任务的触觉潜在嵌入。该编码器通过在配备Xela uSkin传感器的Allegro手上采集的多种无标签手-物交互数据上进行自蒸馏，实现自监督训练。在从状态估计到策略学习的多项基准任务实验中，我们发现预训练的Sparsh-skin表示不仅在下游任务学习中具备样本效率，且任务性能相比现有工作提升超过41%，相比端到端学习提升超过56%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
