Mitra, M and Patil, AA and Mothish, G and Kumar, G and Mukhopadhyay, A and Murthy, LRD and Chakraborty, PP and Biswas, P (2024) Multimodal Target Prediction for Rapid Human-Robot Interaction. In: 29th International Conference on Intelligent User Interfaces, IUI 2024, 18 March 2024 to 21 March 2024, Greenville, pp. 18-23.
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Abstract
Intent prediction finds widespread applications in user interface (UI/UX) design to predict target icons, in automotive industry to anticipate driver's intent, and in understanding human motion during human-robot interactions (HRI). Predicting human intent involves analyzing factors such as hand motion, eye gaze movement, and gestures. This paper introduces a multimodal intent prediction algorithm involving hand and eye gaze using Bayesian fusion. Inverse reinforcement learning was leveraged to learn human preferences for the human-robot handover task. Results demonstrate that the proposed approach achieves the highest prediction accuracy of 99.9 at 60 task completion as compared to state-of-the-art (SOTA) methods. © 2024 Owner/Author.
| Item Type: | Conference Paper |
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| Publication: | ACM International Conference Proceeding Series |
| Publisher: | Association for Computing Machinery |
| Additional Information: | The copyright for this article belongs to Authors. |
| Keywords: | Automotive industry; Human robot interaction; Inverse problems; Microrobots; Prediction models; Reinforcement learning; Robot learning, Automotives; Eye-gaze; Hand motion; Human motions; Humans-robot interactions; Intent prediction; Inverse reinforcement learning; Multi-modal; Multimodal target prediction; Target prediction |
| Department/Centre: | Division of Interdisciplinary Sciences > Robert Bosch Centre for Cyber Physical Systems Division of Mechanical Sciences > Department of Design & Manufacturing (formerly Centre for Product Design & Manufacturing) |
| Date Deposited: | 21 Oct 2024 11:53 |
| Last Modified: | 21 Oct 2024 11:53 |
| URI: | http://eprints.iisc.ac.in/id/eprint/86493 |
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