Soft In-Hand Manipulation
The morphological properties of dexterous soft hands offer many advantages in the accomplishment of contact-rich manipulation tasks. Nevertheless, classical analytic and learning-based approaches are not directly applicable because they were developed with a rigid hand paradigm for manipulation in mind.
To meet this challenge, we want to develop controls that exploit the compliance of the hand in contact scenarios. This requires an understanding of the morphological computation (MC) the hand is performing in manipulation tasks. Given a measure for MC, we want to find control-sequences that exploit the beneficial MC of the hand. By beneficial, we refer to MC that supports/facilitates in-hand manipulation. Bringing together the computation performed by the hand and the computation performed by a controller, we hope to discover "funnels" for in-hand manipulation that represent robust, repeatable and sequencable manipulation skills.
This effort will include investigations of different feature encodings, sample-efficient learning algorithms, appropriate simulation environments and various kinds of hand sensorization.
An Emperical Investigation of Soft In-Hand Manipulation
Open-Loop Cube Spelling Demonstration
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Project description
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. The are also very insensitive in 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 derive three principles that we believe to be an important foundation for robust robotic in-hand manipulation, and possibly for manipulation in general.
Project Summary
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Funding
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2002/1 "Science of Intelligence" - project number 390523135 - Dexterous and Sensorized Soft Robotic Hands, May 2019 - April 2022
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Priority Programme SPP 2100: Soft Material Robotic System - project number: 405033880
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Robotics-Specific Machine Learning (R-ML) - project number 3294260
- SoMa - Soft Manipulation, Horizon 2020 project funded by the European Commission. May 2015 - April 2019.
- Alexander von Humboldt professorship - awarded by the Alexander von Humboldt foundation and funded through the Ministry of Education and Research, BMBF, July 2009 - June 2015
