Understanding Human Manipulation with the Environment

A Novel Taxonomy for Video Labelling

Journal article (2021)

Authors

Visar Arapi University of Klagenfurt

C. Della Santina Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering

Giuseppe Averta University of Pisa

Antonio Bicchi University of Pisa

Matteo Bianchi University of Pisa

Research Group

Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)

DOI: https://doi.org/10.1109/LRA.2021.3094246

Deep learning Bimanual manipulation Datasets for human motion Dexterous manipulation

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Published Date

2021

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

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.

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