A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths

Abstract

As human-robot interaction rapidly spreads in numerous fields, the subject of robot acceptance gains increasing importance. Visual similarity to the human body, as occurs for humanoids, is generally not enough to guarantee acceptance in physical interaction, as acceptance directly links to comfort and ergonomics, which are measured in terms of the quality of the robot movement perceived by the human. This paper discusses the connection between comfort and similarity of the robot movement to the human one. By considering the kinematic characterization of human movement, this paper focuses on the time laws of such movements, wherein the end-effector path is prescribed. Based on the sigma-lognormal velocity model, a human-likeness index is defined and used to provide with an a priori characterization of trajectories. Such an index can be used to evaluate the performance of trajectory generation algorithms in producing human-like movements, before they are actually executed. For validation purposes, through physical interaction with a robot, a sample of 38 subjects is asked to compare trajectories and judge about their comfort over three experimental campaigns. The results demonstrate a globally consistent trend between the preference in terms of perceived comfort and the distribution of the suggested human-likeness index.