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.

翻译：本文提出并解决了多用途机械手两指间小物体的受控分离这一全新任务：在从盛有小物体的盒子中抓取后，目标是将其中尽可能多的小物体丢弃，直至两指间保留所需数量。相较于手指宽度乃至绝对尺寸，这些物体均属微小范畴。本研究中处理的是直径仅为6毫米的微小颗粒。我们证明，该任务可仅凭指尖高空间分辨率触觉皮肤（无需视觉）完成。通过强化学习结合简洁的稀疏奖励（仅需验证是否达到预期物体数量）在仿真环境中训练分离策略。仿真实验全面揭示了空间分辨率触觉反馈的优越性：理想（高分辨率）触觉传感器几乎能完美解决该任务，而低空间分辨率传感器（本文采用4×4触觉点阵）相较于仅使用手指关节传感器，仍可带来高达20%的性能提升。为进行此项分析，我们还训练了与策略并行的估计器，用于预测真实接触位置。最终，我们在配备触觉皮肤的DLR手爪II上成功实现了仿真到现实的迁移。