Large language model-driven natural language interaction control framework for single-operator bimanual teleoperation

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."