@article {3621, title = {Learning From Humans How to Grasp: A Data-Driven Architecture for Autonomous Grasping With Anthropomorphic Soft Hands}, journal = {IEEE Robotics and Automation Letters}, volume = {4}, year = {2019}, month = {April}, pages = {1533-1540}, keywords = {Computer architecture, Control, Deep Learning in Robotics and Automation, Grasping, Learning for Soft Robots, Modeling, Natural Machine Motion, Neural networks, Robot sensing systems, Uncertainty, Videos}, issn = {2377-3766}, doi = {10.1109/LRA.2019.2896485}, author = {C. D. Santina and V. Arapi and G. Averta and F. Damiani and G. Fiore and A Settimi and M. G. Catalano and D. Bacciu and A. Bicchi and M. Bianchi} } @conference {3365, title = {From humans to robots: The role of cutaneous impairment in human environmental constraint exploitation to inform the design of robotic hands}, booktitle = {Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2017 9th International Congress on}, year = {2017}, note = {

This work has been awarded with the "Best Student Paper Award" and the "Best Paper in Session - Robotics"

}, pages = {179{\textendash}184}, publisher = {IEEE}, organization = {IEEE}, address = {Munich}, abstract = {

Human hands are capable of a variety of movements, thanks to their extraordinary biomechanical structure and rely- ing on the richness of human tactile information. Recently, soft robotic hands have opened exciting possibilities and, at the same time, new issues related to planning and control. In this work, we propose to study human strategies in environmental constraint exploitation to grasp objects from a table. We have considered both the case where participants{\textquoteright} fingertips were free and with a rigid shell worn on them to understand the role of cutaneous touch. Main kinematic strategies were quantified and classified in an unsupervised manner. The principal strategies appear to be consistent in both experimental conditions, although cluster cardinality differs. Furthermore, as expected, tactile feedback improves both grasp precision and quality performance. Results opens interesting perspective for sensing and control of soft manipulators.

}, author = {G. Averta and C. Della Santina and E. Battaglia and S. Ciotti and V. Arapi and S. Fani and M. Bianchi} } @article {3216, title = {Postural Hand Synergies during Environmental Constraint Exploitation}, journal = {Fronters in Neurorobotics}, year = {2017}, month = {08/2017}, abstract = {

Humans are able to intuitively exploit the shape of an object and environmental constraints to achieve stable grasps and perform dexterous manipulations. In doing that, a vast range of kinematic strategies can be observed. However, in this work we formulate the hypothesis that such ability can be described in terms of a synergistic behavior in the generation of hand postures, i.e., using a reduced set of commonly used kinematic patterns. This is in analogy with previous studies showing the presence of such behavior in different tasks, such as grasping. We investigated this hypothesis in experiments performed by six subjects, who were asked to grasp objects from a flat surface. We quantitatively characterized hand posture behavior from a kinematic perspective, i.e., the hand joint angles, in both pre-shaping and during the interaction with the environment. To determine the role of tactile feedback, we repeated the same experiments but with subjects wearing a rigid shell on the fingertips to reduce cutaneous afferent inputs. Results show the persistence of at least two postural synergies in all the considered experimental conditions and phases. Tactile impairment does not alter significantly the first two synergies, and contact with the environment generates a change only for higher order Principal Components. A good match also arises between the first synergy found in our analysis and the first synergy of grasping as quantified by previous work. The present study is motivated by the interest of learning from the human example, extracting lessons that can be applied in robot design and control. Thus, we conclude with a discussion on implications for robotics of our findings.

}, keywords = {Haptics, Robotics}, doi = {https://doi.org/10.3389/fnbot.2017.00041}, url = {https://www.frontiersin.org/articles/10.3389/fnbot.2017.00041/full}, author = {C. Della Santina and M. Bianchi and G. Averta and S. Ciotti and V. Arapi and S. Fani and E. Battaglia and M. G. Catalano and M. Santello and A. Bicchi} }