Figure - available from: Frontiers in Robotics and AI
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Acquisition setup (left), observation experiment setup (center), interaction experiment setup (right).
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A crucial aspect in human-robot collaboration is the robot acceptance by human co-workers. Based on previous experiences of interaction with their fellow beings, humans are able to recognize natural movements of their companions and associate them with the concepts of trust and acceptance. Throughout this process, the judgment is influenced by seve...
Citations
... What about non-contact cases? In a previous experiment (Fig. 1b) [5], we asked participants to compare human-recorded and artificial movements of a hand-with-pen avatar writing on a canvas and conclude on which, in the pair, looked more human-like. Notably, in this setup, humans were not able to reliably distinguish between human and artificial movements. ...
... The experiments are designed in the form of comparison tests, in which human subjects are presented with two handwriting motions and asked to select the most comfortable one according to their self-evaluation [40]. Comparison tests are preferred because previous research has demonstrated that humans struggle to identify whether movements are produced by artificial or human agents when they lack a reference [41]. Moreover, formulating the question in terms of comfort, instead of similarity to a human, allows for the subject to express a bias-free preference. ...
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.
In the quest for understanding the factors that contribute to the adoption of the Intentional Stance towards robots, the obvious candidate is the robot itself—its physical features and the way it behaves. This chapter explores how the characteristics of a robot may influence the propensity to adopt the Intentional Stance. It discusses factors such as robot appearance, human-like behaviours, biologically inspired kinematics, and responsiveness to environmental signals, as cues facilitating the perception of the robot as an intentional agent. Variability and temporal parameters in human-like behaviours seem to be crucial to perceive a robot as human-like. Also, the integration of biologically inspired animation principles enhances human-likeness in robot behaviours. While humanoid robots endowed with such features and behaviours improve natural interaction (and thereby, possibly evoking the adoption of the Intentional Stance), the chapter highlights the tension between enhancing anthropomorphism through human-like features and triggering fear reactions due to the Uncanny Valley effect. The chapter highlights the importance of using both implicit measures and explicit judgements for evaluating robot behaviour’s impact on human perception, due to the fact that certain brain mechanisms evoked by a robot might not reach conscious awareness and thus might not be available to explicit reports.
