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| Title: | Quality-focused Active Adversarial Policy for Safe Grasping in Human-Robot Interaction |
| Authors: | Li, Chenghao
Beuran, Razvan
Chong, Nak Young |
| Keywords: | Robot grasping
human-robot interaction
grasp quality score
deep learning
adversarial attack |
| Issue Date: | 2025-10-28 |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Magazine name: | IEEE Transactions on Automation Science and Engineering |
| Start page: | 1 |
| End page: | 1 |
| DOI: | 10.1109/TASE.2025.3626542 |
| Abstract: | Vision-guided robot grasping methods based on Deep Neural Networks (DNNs) have achieved remarkable success in handling unknown objects, attributable to their powerful generalizability. However, these methods with this generalizability tend to recognize the human hand and its adjacent objects as graspable targets, compromising safety during Human-Robot Interaction (HRI). In this work, we propose the Quality-focused Active Adversarial Policy (QFAAP) to solve this problem. Specifically, the first part is the Adversarial Quality Patch (AQP), wherein we design the adversarial quality patch loss and leverage the grasp dataset to optimize a patch with high quality scores. Next, we construct the Projected Quality Gradient Descent (PQGD) and integrate it with the AQP, which contains only the hand region within each real-time frame, endowing the AQP with fast adaptability to the human hand shape. Through AQP and PQGD, the hand can be actively adversarial with the surrounding objects, lowering their quality scores. Therefore, further setting the quality score of the hand to zero will reduce the grasping priority of both the hand and its adjacent objects, enabling the robot to grasp other objects away from the hand without emergency stops. We conduct extensive experiments on the benchmark datasets and a cobot, showing the effectiveness of QFAAP. Our code and demo videos are available in the supplementary items. |
| Rights: | This is the author's version of the work. Copyright (C) 2025 IEEE. IEEE Transactions on Automation Science and Engineering (Early Access). DOI: https://doi.org/10.1109/TASE.2025.3626542. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| URI: | https://hdl.handle.net/10119/20060 |
| Material Type: | author |
| Appears in Collections: | b10-1. 雑誌掲載論文 (Journal Articles)
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| I-CHONG-N-1030.pdf | | 5893Kb | Adobe PDF | View/Open |
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