Dexterous Soft Hands Linearize Feedback-Control for In-Hand Manipulation

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

Erstaunlich robuste In-Hand-Manipulation: eine empirische Studie - ergänzendes Material

This file contains the Appendix accompanying the paper "Surprisingly Robust In-Hand Manipulation: An Empirical Study"This Appendix accompanies the paper "Surprisingly Robust In-Hand Manipulation: An Empirical Study" [1]. Abstract: We present in-hand manipulation skills on a dexterous, compliant, anthropomorphic hand. Even though these skills were derived in a simplistic manner, they exhibit surprising robustness to variations in shape, size, weight, and placement of the manipulated object. They are also very insensitive to variation of execution speeds, ranging from highly dynamic to quasi-static.The robustness of the skills leads to compositional properties that enable extended and robust manipulation programs. To explain the surprising robustness of the in-hand manipulation skills, we performed a detailed, empirical analysis of the skills’ performance. From this analysis, we identify three principles for skill design: 1) Exploiting the hardware’s innate abilityto drive hard-to-model contact dynamics. 2) Taking actions to constrain these interactions, funneling the system into a narrow set of possibilities. 3) Composing such action sequences into complex manipulation programs. We believe that these principles constitute an important foundation for robust robotic in-hand manipulation, and possibly for manipulation in general. [1] Aditya Bhatt, Adrian Sieler, Steffen Puhlmann and Oliver Brock. "Surprisingly Robust In-Hand Manipulation: An Empirical Study," in Proceedings of Robotics: Science and Systems, 2021.DFG, 390523135, EXC 2002: Science of Intelligence (SCIoI)DFG, 405033880, SPP 2100: Soft Material Robotic SystemsDFG, 405033880, Co-Design von sensorbasierter Regelung und weicher Morphologie für InhandmanipulationDFG, 329426068, Maschinelles Lernen für Probleme in der Roboti