This paper presents a feedback-control framework for in-hand manipulation (IHM) with dexterous soft hands that enables the acquisition of manipulation skills in the real-world within minutes. We choose the deformation state of the soft hand as the control variable. To control for a desired deformation state, we use coarsely approximated Jacobians of the actuation-deformation dynamics. These Jacobian are obtained via explorative actions. This is enabled by the self-stabilizing properties of compliant hands, which allow us to use linear feedback control in the presence of complex contact dynamics. To evaluate the effectiveness of our approach, we show the generalization capabilities for a learned manipulation skill to variations in object size by 100 %, 360 degree changes in palm inclination and to disabling up to 50 % of the involved actuators. In addition, complex manipulations can be obtained by sequencing such feedback-skills.

翻译：本文提出一种用于灵巧软体手掌内操作（IHM）的反馈控制框架，能够在数分钟内于真实环境中习得操作技能。我们选择软体手的形变状态作为控制变量。为实现期望形变状态的控制，采用基于探索性动作获得的驱动-形变动力学的粗近似雅可比矩阵。这种方法的可行性得益于顺应性手的自稳定特性，使得在复杂接触动力学条件下仍能应用线性反馈控制。为评估方法有效性，我们展示了习得操作技能对以下变化的泛化能力：物体尺寸变化100%、手掌倾角360度旋转，以及禁用多达50%的参与驱动器。此外，通过串联此类反馈技能可实现复杂操作。