1 research outputs found
Human-Assisted Continual Robot Learning with Foundation Models
Large Language Models (LLMs) have been shown to act like planners that can
decompose high-level instructions into a sequence of executable instructions.
However, current LLM-based planners are only able to operate with a fixed set
of skills. We overcome this critical limitation and present a method for using
LLM-based planners to query new skills and teach robots these skills in a data
and time-efficient manner for rigid object manipulation. Our system can re-use
newly acquired skills for future tasks, demonstrating the potential of open
world and lifelong learning. We evaluate the proposed framework on multiple
tasks in simulation and the real world. Videos are available at:
https://sites.google.com/mit.edu/halp-robot-learning