ChatHRC: Personalized Human-Robot Collaboration using Fuzzy Reinforcement Learning with Natural Language Rewards

Collaboration between humans and robots can be challenging because robots may have difficulty understanding a specific person's intentions, particularly in complicated tasks such as co-manipulation and assembly in computer, communication, and consumer electronics (3C) manufacturing. These tasks require different weights on accuracy and speed for various fabrication steps, making traditional physical interaction inadequate. In this paper, we introduce a fuzzy reinforcement learning-based admittance controller that can infer humans' intentions not only through physical interaction but also through natural language. During training, the natural language is encoded into a reward term to help the robot reach the human-intended convergence point, allowing us to develop a 'personalized' policy. During testing, the language serves as a tool to help the robot understand and obey humans' intentions when physical interaction alone is insufficient. For example, if the user finds it difficult to push the robot and needs it to move faster, they can say 'it's really slow,' while a request for high-accuracy operation can be conveyed through 'the damping is too small.' With this algorithm, the robot can comprehend the intentions and act accordingly in such situations. Further results and videos can be found at: https://sites.google.com/view/hri-nlp. © 2023 IEEE.