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

Rosanna Coccaro, Enrico Ferrentino, Antonio Parziale, Angelo Marcelli, Pasquale Chiacchio · N/A · 2026

Summary

As human-robot interaction rapidly spreads in numerous fields, the subject of robot acceptance gains increasing importance.

Abstract Summary

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 ensure 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 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.

Key Points

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 ensure 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 lognormality principle for modeling human movements, a human-likeness index is defined and used to provide an a priori characterization of trajectories.

Materials and methods

Given that the concept of “behavior” covers a range of different aspects, there is to specify that this paper is concerned with the kinematic characteristics of movement. Among these, we focus on the time law that underlies movement, assuming that the geometrical features of said motion are already defined.

Index validation setup

In virtue of Hypothesis 1 and our definition of the human-likeness index in ( 12 ), we would like to answer the following questions. Given two movements with significantly different values of $HL$, is the one with the largest value of $HL$ recognized as more comfortable than the other? Given two movements with similar $HL$ values, are they indistinguishable from the comfort standpoint?

For such validation, we recruited participants among the members of the Robotics Laboratory and students enrolled in various STEM and non-STEM curricula from the...

Index validation setup — A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths — (a)

Experimental Setup

Human-Uniform , involves the robot executing 30 trajectories, in the form of 15 pairs (5 per pattern), each composed of a Human and its Uniform counterpart. Human-STOTPAC , involves the robot executing 30 trajectories, in the form of 15 pairs (5 per pattern), each composed of a Human and its STOTPAC counterpart.

Human-Human , involves the robot executing 36 trajectories, in the form of 18 pairs (6 per pattern), each composed of a Human with higher $HL$ and a Human with lower $HL$.

Experimental Setup — A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths — (b)

Results Analysis

The outcomes of the experiments, in terms of preference rate for the most comfortable trajectory in a pair, for the three campaigns, are summarized in Fig. 5 . Except for one outlier associated with the EU pattern, i.e. Fig. 5 (bottom), our trials show a consistent preference for the Human trajectories in the Human-Uniform comparisons.

When replacing the Uniform profile with the STOTPAC one, the participants still globally prefer the Human trajectories, but, in several trials, they feel STOTPAC trajectories more comfortable than Human ones.

Results Analysis — A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths — (c)

Sources and Demonstrations

Figures are reproduced from Coccaro et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.