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.Comment: Accepted at 2023 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS