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Hand-arm autonomous grasping: synergistic motions to enhance the learning process

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Abstract

In this paper, a reinforcement learning algorithm based on policy search methods has been developed for an anthropomorphic hand-arm system. The advantages of searching in the space of a policy are that the convergence and fastness of the algorithm rely on the proper choice of the policy and of its initial parameters together with the choice of the reward function. The main contribution of the paper consists in the computation of a suitable policy based on the synergies of the hand-arm system, and in the development of a supervised learning strategy to define the initial parameters of the policy. Finally, the reward function has been chosen to evaluate the goodness of the grasp in a synergy-based framework. The experiments executed on the robotic hand-arm system, constituted by the SCHUNK 5-Finger Hand and the KUKA LWR4+, demonstrate the convergence of the method and the promising results obtained for the execution of grasping tasks.

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Acknowledgements

The author wants to thank Damiano Zaccara which contributed to the development of the set-up and supported the experiments described in this paper.

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  1. Dipartimento di Ingegneria Elettrica e Tecnologie dell’Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125, Naples, Italy

    Fanny Ficuciello

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Correspondence to Fanny Ficuciello.

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A preliminary version of this work has been published in IROS 2016 Proceedings. The research leading to these results has been partially supported by the RoDyMan project, funded by the European Community’s Seventh Framework Programme (FP7/2007-2013) under ERC AdG-320992, and partially by MUSHA project, National Italian Grant under Programma STAR Linea 1.

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Ficuciello, F. Hand-arm autonomous grasping: synergistic motions to enhance the learning process. Intel Serv Robotics 12, 17–25 (2019). https://doi.org/10.1007/s11370-018-0262-0

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