Human-in-the-loop Learning for Adaptive Robot Manipulation using Large Language Models and Behavior Trees

Abstract

Large Language Models (LLMs) are now transforming the way robots learn to work in unpredictable environments, such as homes or small enterprises. A growing number of approaches are combining LLMs with Behavior Trees (BTs). Not only do user commands need to be interpreted into BTs that contain the task’s goal, but external disturbances also need to be handled during the process when BT planners dynamically expand BTs based on action databases. However, in these approaches, the action database is manually pre-built and requires the capability for incremental learning and expansion. To address this issue, we propose a human-in-the-loop learning mechanism. First, we design a context for the LLM and then use it to generate action knowledge through in-context learning. In addition, we introduce human-in-the-loop. User feedback is utilized to guide the LLM to correct and refine the action knowledge, ensuring its accuracy and safety. Finally, the generated action knowledge can be directly used for adaptive manipulation without the need for knowledge transfer effort, enabling the robot to complete tasks and handle external disturbances. Experiments across various tasks are conducted and the experimental results validate our method.