The need for users’ safety and technology acceptability has incredibly increased with the deployment of co-bots physically interacting with humans in industrial settings, and for people assistance. A well-studied approach to meet these requirements is to ensure human-like robot motions and interactions. In this manuscript, we present a research approach that moves from the understanding of human movements and derives usefull guidelines for the planning of arm movements and the learning of skills for physical interaction of robots with the surrounding environment.

In recent years, the spread of data-driven approaches for robotic grasp synthesis has come with the increasing need for reliable datasets, which can be built e.g. through video labelling. To this goal, it is important to define suitable rules to characterize the main human grasp types, for easily identifying them in video streams. In this work, we present a novel taxonomy that builds upon the related state of the art, but it is specifically thought for video labelling. It focuses on the interaction of the hand with the environment and accounts for pre-contact phases, bi-manual grasps as well as non-prehensile strategies. This study is complemented with a dataset of labelled videos of subjects performing activities of daily living, for a total of nine hours, and the description of MatLab tools for labelling new videos. Both hands were labelled at any time. We used these labelled data for performing a preliminary statistical description of the occurrences of the here proposed class types.