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Context-Awareness and Biologically Inspired Behaviour Based on Attention Mechanisms for Natural Human-Robot
Jesús García-Martínez1, Marcos Maroto-Gómez1, Arecia Segura-Bencomo1
1Systems Engineering and Automation Department, Universidad Carlos III de Madrid, Avenida de la Universidad, 30, 28911 Leganés, Madrid, Spain.
Biomimetics (Basel, Switzerland)
|May 26, 2026
Summary
This study introduces a biologically inspired system for robots to communicate intentions, enhancing human-robot interaction. Robots using this system were perceived as more sociable and less disturbing, improving natural engagement.
Area of Science:
- Robotics
- Human-Computer Interaction
- Artificial Intelligence
Background:
- Effective human-robot interaction (HRI) relies on clear robot communication of intentions and needs.
- Biologically inspired models can enhance mutual understanding by incorporating contextual information.
- Current HRI systems often lack naturalistic communication, leading to suboptimal interactions.
Purpose of the Study:
- To present a Context-Awareness and Biologically Inspired Behaviour system for more natural HRI.
- To develop a system that generates context- and goal-adapted verbal and non-verbal interaction.
- To improve robot expressiveness and reduce user discomfort.
Main Methods:
- Developed a system combining sensory information processing via a Joint Attention System with task-related motivations.
- Integrated internal processes and prioritization of stimuli for adaptive responses.
- Evaluated the system using a video-based user study comparing two robots with differing behavioral approaches.
Main Results:
- Participants rated the robot with the proposed system as significantly more sociable, agentic, and animated.
- The robot utilizing the internal state and joint attention mechanisms demonstrated improved interaction quality.
- The absence of the proposed system's responses led to a perception of the robot as more disturbing.
Conclusions:
- The Context-Awareness and Biologically Inspired Behaviour system significantly enhances naturalness in HRI.
- Biologically inspired mechanisms for internal state representation and joint attention improve robot expressiveness and user perception.
- This approach offers a pathway to more intuitive and less disturbing robot interactions.
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