Abstract
This paper presents a novel approach to enhance the social interaction capabilities of the ARI humanoid robot using reinforcement learning. We focus on enabling ARI to imitate human attention/gaze behaviour by identifying salient points in dynamic environments, employing the Zero-Shot Transfer technique combined with domain randomisation and generalisation. Our methodology uses the Proximal Policy Optimisation algorithm, training the reinforcement learning agent in a simulated environment to maximise robustness in real-world scenarios. We demonstrated the efficacy of our approach by deploying the trained agent on the ARI humanoid and validating its performance in human-robot interaction scenarios. The results indicated that using the developed model, ARI can successfully identify and respond to salient points, exhibiting human-like attention/gaze behaviours, which is an important step towards acceptability and efficiency in humanrobot interactions. This research contributes to advancing the capabilities of social robots in dynamic and unpredictable environments, highlighting the potential of combining ZeroShot Transfer with domain randomisation and generalisation for robust real-world applications.
Original language | English |
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Title of host publication | 2024 IEEE-RAS 23rd International Conference on Humanoid Robots, Humanoids 2024 |
Place of Publication | France |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 653-660 |
Number of pages | 8 |
ISBN (Electronic) | 979-8-3503-7357-8 |
DOIs | |
Publication status | Published - 3 Dec 2024 |
Event | 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids) - Nancy, France Duration: 22 Nov 2024 → 24 Nov 2024 Conference number: 23 https://2024.ieee-humanoids.org/ |
Publication series
Name | IEEE-RAS International Conference on Humanoid Robots |
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ISSN (Print) | 2164-0572 |
ISSN (Electronic) | 2164-0580 |
Conference
Conference | 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids) |
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Abbreviated title | 2024 IEEE-RAS |
Country/Territory | France |
City | Nancy |
Period | 22/11/24 → 24/11/24 |
Internet address |