用仿生触觉皮肤+仿真训练，让机械手实现毫米级精细操作。
Fine Manipulation Using a Tactile Skin: Learning in Simulation and Sim-to-Real Transfer
- 设计可适配快速物理引擎的仿生触觉皮肤模型
- 仿真中实现<1mm精度（税点间距4mm）
- 策略成功从仿真迁移到真实机械手
我们旨在通过现代深度强化学习方法，使多指机械手实现精细操作。精细操作的关键在于具有空间分辨能力的触觉传感器。本文提出一种新型触觉皮肤模型，可与刚体物理模拟器协同使用。该模型考虑了真实指尖的柔软性，使接触力能根据接触几何分布在多个税点上。我们通过自包含校准方法，无需外部工具或传感器，准确匹配真实传感器特性。为验证方法有效性，我们在仿真中学习了两个高难度任务：在两指间滚动弹珠和螺栓。实验表明，触觉反馈对精确操作至关重要，在仅4mm税点间距条件下实现了<1mm的亚税点分辨率。此外，所有策略均成功从仿真迁移到真实机械手。
原文摘要 · Abstract (English)
We want to enable fine manipulation with a multi-fingered robotic hand by using modern deep reinforcement learning methods. Key for fine manipulation is a spatially resolved tactile sensor. Here, we present a novel model of a tactile skin that can be used together with rigid-body (hence fast) physics simulators. The model considers the softness of the real fingertips such that a contact can spread across multiple taxels of the sensor depending on the contact geometry. We calibrate the model parameters to allow for an accurate simulation of the real-world sensor. For this, we present a self-contained calibration method without external tools or sensors. To demonstrate the validity of our approach, we learn two challenging fine manipulation tasks: Rolling a marble and a bolt between two fingers. We show in simulation experiments that tactile feedback is crucial for precise manipulation and reaching sub-taxel resolution of < 1 mm (despite a taxel spacing of 4 mm). Moreover, we demonstrate that all policies successfully transfer from the simulation to the real robotic hand.
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