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.
Soft hands are robotic systems that embed compliant elements in their mechanical design. This enables an effective adaptation with the items and the environment, and ultimately, an increase in their grasping performance. These hands come with clear advantages in terms of ease-to-use and robustness if compared with classic rigid hands, when operated by a human. However, their potential for autonomous grasping is still largely unexplored, due to the lack of suitable control strategies. To address this issue, in this letter, we propose an approach to enable soft hands to autonomously grasp objects, starting from the observations of human strategies. A classifier realized through a deep neural network takes as input the visual information on the object to be grasped, and predicts which action a human would perform to achieve the goal. This information is hence used to select one among a set of human-inspired primitives, which define the evolution of the soft hand posture as a combination of anticipatory action and touch-based reactive grasp. The architecture is completed by the hardware component, which consists of an RGB camera to look at the scene, a 7-DoF manipulator, and a soft hand. The latter is equipped with inertial measurement units at the fingernails for detecting contact with the object. We extensively tested the proposed
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