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

As human-robot interaction (HRI) rapidly spreads into numerous fields, the subject of robot acceptance gains increasing importance. Visual similarity to the human body, as occurs with humanoids, is generally not enough to ensure acceptance in physical interaction, as acceptance is directly linked to comfort and ergonomics, which are measured in terms of the quality of the robot movement perceived by the human. This article discusses the connection between comfort and the similarity of the robot movement to the human movement. By considering the kinematic characterization of human movement, this article focuses on the time laws of such movements, wherein the end-effector path is prescribed. Based on the lognormality principle for modeling human movements, a human-likeness index is defined and used to provide 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, 68 subjects are required to judge their comfort. The results of three experimental campaigns involving a physical interaction with a robot demonstrate a globally consistent trend between the preference in terms of perceived comfort and the distribution of the suggested human-likeness index.