Authors
Haolin Fei, Tao Xue, Yiyang He, Sheng Lin, Guanglong Du, Yao Guo, Ziwei Wang
Published in
Frontiers in robotics and AI. Volume 12. Pages 1621033. Epub Jul 17, 2025.
Abstract
Bimanual teleoperation imposes cognitive and coordination demands on a single human operator tasked with simultaneously controlling two robotic arms. Although assigning each arm to a separate operator can distribute workload, it often leads to ambiguities in decision authority and degrades overall efficiency. To overcome these challenges, we propose a novel bimanual teleoperation large language model assistant (BTLA) framework, an intelligent co-pilot that augments a single operator's motor control capabilities. In particular, BTLA enables operators to directly control one robotic arm through conventional teleoperation while directing a second assistive arm via simple voice commands, and therefore commanding two robotic arms simultaneously. By integrating the GPT-3.5-turbo model, BTLA interprets contextual voice instructions and autonomously selects among six predefined manipulation skills, including real-time mirroring, trajectory following, and autonomous object grasping. Experimental evaluations in bimanual object manipulation tasks demonstrate that BTLA increased task coverage by 76.1 and success rate by 240.8 relative to solo teleoperation, and outperformed dyadic control with a 19.4 gain in coverage and a 69.9 gain in success. Furthermore, NASA Task Load Index (NASA-TLX) assessments revealed a 38-52 reduction in operator mental workload, and 85 of participants rated the voice-based interaction as "natural" and "highly effective."
PMID:
40747446
Bibliographic data and abstract were imported from PubMed on 01 Aug 2025.
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