1
|
Scandola M, Cross ES, Caruana N, Tidoni E. Body Form Modulates the Prediction of Human and Artificial Behaviour from Gaze Observation. Int J Soc Robot 2023. [DOI: 10.1007/s12369-022-00962-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
AbstractThe future of human–robot collaboration relies on people’s ability to understand and predict robots' actions. The machine-like appearance of robots, as well as contextual information, may influence people’s ability to anticipate the behaviour of robots. We conducted six separate experiments to investigate how spatial cues and task instructions modulate people’s ability to understand what a robot is doing. Participants observed goal-directed and non-goal directed gaze shifts made by human and robot agents, as well as directional cues displayed by a triangle. We report that biasing an observer's attention, by showing just one object an agent can interact with, can improve people’s ability to understand what humanoid robots will do. Crucially, this cue had no impact on people’s ability to predict the upcoming behaviour of the triangle. Moreover, task instructions that focus on the visual and motor consequences of the observed gaze were found to influence mentalising abilities. We suggest that the human-like shape of an agent and its physical capabilities facilitate the prediction of an upcoming action. The reported findings expand current models of gaze perception and may have important implications for human–human and human–robot collaboration.
Collapse
|
2
|
Mukherjee D, Gupta K, Najjaran H. A Critical Analysis of Industrial Human-Robot Communication and Its Quest for Naturalness Through the Lens of Complexity Theory. Front Robot AI 2022; 9:870477. [PMID: 35899077 PMCID: PMC9309351 DOI: 10.3389/frobt.2022.870477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 06/21/2022] [Indexed: 11/13/2022] Open
Abstract
Human-robot communication is one of the actively researched fields to enable efficient and seamless collaboration between a human and an intelligent industrial robotic system. The field finds its roots in human communication with the aim to achieve the “naturalness” inherent in the latter. Industrial human-robot communication pursues communication with simplistic commands and gestures, which is not representative of an uncontrolled real-world industrial environment. In addition, naturalness in communication is a consequence of its dynamism, typically ignored as a design criterion in industrial human-robot communication. Complexity Theory-based natural communication models allow for a more accurate representation of human communication which, when adapted, could also benefit the field of human-robot communication. This paper presents a perspective by reviewing the state of human-robot communication in industrial settings and then presents a critical analysis of the same through the lens of Complexity Theory. Furthermore, the work identifies research gaps in the aforementioned field, fulfilling which, would propel the field towards a truly natural form of communication. Finally, the work briefly discusses a general framework that leverages the experiential learning of data-based techniques and naturalness of human knowledge.
Collapse
Affiliation(s)
- Debasmita Mukherjee
- Advanced Control and Intelligent Systems Laboratory, School of Engineering, The University of British Columbia, Kelowna, BC, Canada
| | - Kashish Gupta
- Advanced Control and Intelligent Systems Laboratory, Faculty of Engineering, University of Victoria, Victoria, BC, Canada
| | - Homayoun Najjaran
- Advanced Control and Intelligent Systems Laboratory, Faculty of Engineering, University of Victoria, Victoria, BC, Canada
- *Correspondence: Homayoun Najjaran,
| |
Collapse
|