Mattia Robbiani

Hengjia Xiao, Peng Wang, Mingzhe Yu et al.

This research focuses on how Large Language Models (LLMs) can help with (path) planning for mobile embodied agents such as robots, in a human-in-the-loop and interactive manner. A novel framework named LLM A*, aims to leverage the commonsense of LLMs, and the utility-optimal A* is proposed to facilitate few-shot near-optimal path planning. Prompts are used for two main purposes: 1) to provide LLMs with essential information like environments, costs, heuristics, etc.; 2) to communicate human feedback on intermediate planning results to LLMs. This approach takes human feedback on board and renders the entire planning process transparent (akin to a `white box') to humans. Moreover, it facilitates code-free path planning, thereby fostering the accessibility and inclusiveness of artificial intelligence techniques to communities less proficient in coding. Comparative analysis against A* and RL demonstrates that LLM A* exhibits greater efficiency in terms of search space and achieves paths comparable to A* while outperforming RL. The interactive nature of LLM A* also makes it a promising tool for deployment in collaborative human-robot tasks. Codes and Supplemental Materials can be found at GitHub: https://github.com/speedhawk/LLM-A-.

Peng Wang, Mattia Robbiani, Zhihao Guo

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.