Abstract
In this work, a supervised learning strategy has been applied in conjunction with a control strategy to provide anthropomorphic hand-arm systems with autonomous grasping capabilities. Both learning and control algorithms have been developed in a synergy-based framework in order to address issues related to high dimension of the configuration space, that typically characterizes robotic hands and arms with human-like kinematics. An experimental setup has been built to learn hand-arm motion from humans during reaching and grasping tasks. Then, a Neural Network (NN) has been realized to generalize the grasps learned by imitation. Since the NN approximates the relationship between the object characteristics and the grasp configuration of the hand-arm system, a synergy-based control strategy has been applied to overcome planning errors. The reach-to-grasp strategy has been tested on a setup constituted by the KUKA LWR 4+ Arm and the SCHUNK 5-Finger Hand.
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Ficuciello, F., Federico, A., Lippiello, V., Siciliano, B.: Synergies evaluation of the SCHUNK S5FH for grasping control. In: 15th International Symposium on Advances in Robot Kinematics (2016)
Feix, T., Pawlik, R., Schmiedmayer, H., Romero, J., Kragic, D.: The generation of a comprehensive grasp taxonomy. In: Workshop on Understanding the Human Hand for Advancing Robotic Manipulation, Robotics, Science and Systems, Washington DC (2009)
SCHUNK S5FH: schunk svh driver. http://wiki.ros.org/schunksvhdriver
Palli, G., Melchiorri, C., Vassura, G., Scarcia, U., Moriello, L., Berselli, G., Cavallo, A., Maria, G.D., Natale, C., Pirozzi, S., May, C., Ficuciello, F., Siciliano, B.: The DEXMART hand: mechatronic design and experimental evaluation of synergy-based control for human-like grasping. Int. J. Robot. Res. 33, 799–824 (2014)
Ficuciello, F., Palli, G., Melchiorri, C., Siciliano, B.: Postural synergies of the UB hand IV for human-like grasping. Robot. Auton. Syst. 62, 357–362 (2014)
Ficuciello, F., Palli, G., Melchiorri, C., Siciliano, B.: A model-based strategy for mapping human grasps to robotic hands using synergies. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Wollongong, Australia, pp. 1737–1742 (2013)
Acknowledgments
This research has been partially funded by the EU Seventh Framework Programme (FP7) within RoDyMan project 320992.
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Ficuciello, F., Zaccara, D., Siciliano, B. (2017). Learning Grasps in a Synergy-based Framework. In: Kulić, D., Nakamura, Y., Khatib, O., Venture, G. (eds) 2016 International Symposium on Experimental Robotics. ISER 2016. Springer Proceedings in Advanced Robotics, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-50115-4_12
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DOI: https://doi.org/10.1007/978-3-319-50115-4_12
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