In-hand manipulation planning using human motion dictionary

Ali Hammoud (1) , Valerio Belcamino (2) , Alessandro Carfi (2) , Veronique Perdereau (1) , Fulvio Mastrogiovanni (2)

1 ISIR - Institut des Systèmes Intelligents et de Robotique
2 UniGe - Università degli studi di Genova = University of Genoa

Dexterous in-hand manipulation is a peculiar and useful human skill. This ability requires the coordination of many senses and hand motion to adhere to many constraints. These constraints vary and can be influenced by the object characteristics or the specific application. One of the key elements for a robotic platform to implement reliable inhand manipulation skills is to be able to integrate those constraints in their motion generations. These constraints can be implicitly modelled, learned through experience or human demonstrations. We propose a method based on motion primitives dictionaries to learn and reproduce in-hand manipulation skills. In particular, we focused on fingertip motions during the manipulation, and we defined an optimization process to combine motion primitives to reach specific fingertip configurations. The results of this work show that the proposed approach can generate manipulation motion coherent with the human one and that manipulation constraints are inherited even without an explicit formalization.

Domaines: Robotique [cs.RO]

Ali Hammoud, Valerio Belcamino, Alessandro Carfi, Veronique Perdereau, Fulvio Mastrogiovanni. In-hand manipulation planning using human motion dictionary. 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2022), Aug 2022, Naples & virtual conference, Italy. pp.927-933, ⟨10.1109/RO-MAN53752.2022.9900858⟩. ⟨hal-04609538⟩