用触觉皮肤实现双指精准分离小物件，无需视觉。
Learning Controlled Separation of Small Objects Between Two Fingers with a Tactile Skin
- 纯触觉控制，通过指尖触觉皮肤反馈调节抓握
- 4×4触觉分辨率下仍比仅用关节传感器提升20%
- 适合需要高精度微操作的机器人应用
我们提出并解决了一个新型任务：使用多功能机械手的双指，在抓取装有小物体的盒子后，可控地释放多余物体，使剩余数量达到目标值。物体尺寸很小，直径仅为6mm。实验表明，仅依靠指尖空间分辨触觉皮肤即可完成该任务，无需视觉。分离策略通过强化学习在仿真中训练，采用简单稀疏奖励机制，仅判断是否达到目标数量。仿真分析显示，理想高分辨率触觉传感器几乎可完美完成任务，而4×4触觉单元（taxels）仍比仅依赖关节传感器提升20%性能。此外，同步训练了接触位置估计器以还原真实接触状态。最后，成功实现从仿真到真实DRL-Hand II机械手的迁移，验证了方法可行性。
原文摘要 · Abstract (English)
We introduce and solve the novel task of controlled separation of small objects with two fingers of a multi-purpose robotic hand: after grasping into a box of small objects, the task is to drop as many of them until a desired number remains between the fingers. The objects are small compared to the width of the fingers but also in absolute terms. In our case little pellets with a diameter of only 6mm are handled. We show that the task can be performed purely tactile (no vision) using a spatially-resolved tactile skin on a fingertip. The separation policy is trained in simulation via reinforcement learning using a straightforward sparse reward, which basically checks if the desired number of objects is reached. In simulation experiments, we provide an exhaustive analysis of the benefits of using spatially-resolved tactile feedback: while an ideal (high-resolution) tactile sensor allows solving the task almost perfectly, a sensor with lower spatial resolution (here 4x4 taxels) still leads to an improvement of up to 20% compared to using only the fingers' joint sensors. For this analysis, we further train an estimator alongside the policy that predicts the ground truth contact positions. Finally, we demonstrate the successful sim-to-real transfer for the DLR-Hand II equipped with a tactile skin.
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