Mitra, M and Kumar, G and Chakrabarti, PP and Biswas, P (2024) Enhanced Human-Robot Collaboration with Intent Prediction using Deep Inverse Reinforcement Learning. In: 2024 IEEE International Conference on Robotics and Automation, ICRA 2024, 13 May 2024 through 17 May 2024, Yokohama, pp. 7880-7887.
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Abstract
In shared autonomy, human-robot handover for object delivery is crucial. Accurate robot predictions of human hand motion and intentions enhance collaboration efficiency. However, low prediction accuracy increases mental and physical demands on the user. In this work, we propose a system for predicting hand motion and intended target during human-robot handover using Inverse Reinforcement Learning (IRL). A set of feature functions were designed to explicitly capture users' preferences during the task. The proposed approach was experimentally validated through user studies. Results indicate that the proposed method outperformed other state-of-the-art methods (PI-IRL, BP-HMT, RNNIK-MKF and CMk=5) with users feeling comfortable reaching upto 60 of the total distance to the target for handover with 90 target prediction accuracy. The target prediction accuracy reaches 99.9 when less than 20 of the task remains. © 2024 IEEE.
Item Type: | Conference Paper |
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Publication: | Proceedings - IEEE International Conference on Robotics and Automation |
Publisher: | Institute of Electrical and Electronics Engineers Inc. |
Additional Information: | The copyright for this article belongs to publisher. |
Keywords: | Deep reinforcement learning; Microrobots; Prediction models; Reinforcement learning, Hand motion; Hand over; Human hand motions; Human robots; Human-robot collaboration; Inverse reinforcement learning; Physical demand; Prediction accuracy; Shared autonomy; Target prediction, Adversarial machine learning |
Department/Centre: | Division of Interdisciplinary Sciences > Robert Bosch Centre for Cyber Physical Systems |
Date Deposited: | 12 Sep 2024 07:21 |
Last Modified: | 12 Sep 2024 07:21 |
URI: | http://eprints.iisc.ac.in/id/eprint/86142 |
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