LLM Granularity for On-the-Fly Robot Control
It addresses the problem of enabling assistive robots to operate in visually limited environments for vulnerable individuals like the elderly, but it is incremental as it builds on existing visuolinguomotor modes.
This work investigates whether language alone can control assistive robots when visuals are unreliable, by evaluating responses to language prompts of varying granularities and exploring on-the-fly control, with experiments conducted on a Sawyer cobot and a Turtlebot robot.
Assistive robots have attracted significant attention due to their potential to enhance the quality of life for vulnerable individuals like the elderly. The convergence of computer vision, large language models, and robotics has introduced the `visuolinguomotor' mode for assistive robots, where visuals and linguistics are incorporated into assistive robots to enable proactive and interactive assistance. This raises the question: \textit{In circumstances where visuals become unreliable or unavailable, can we rely solely on language to control robots, i.e., the viability of the `linguomotor` mode for assistive robots?} This work takes the initial steps to answer this question by: 1) evaluating the responses of assistive robots to language prompts of varying granularities; and 2) exploring the necessity and feasibility of controlling the robot on-the-fly. We have designed and conducted experiments on a Sawyer cobot to support our arguments. A Turtlebot robot case is designed to demonstrate the adaptation of the solution to scenarios where assistive robots need to maneuver to assist. Codes will be released on GitHub soon to benefit the community.