Resolving Ambiguities in LLM-enabled Human-Robot Collaboration
Keywords: LLM, Active Learning
Abstract: Large Language Models demonstrate exciting reasoning capabilities that can be utilized in translating user instructions to robot actions in Human-Robot collaboration context. Yet this approach is still prone to failure due to ambiguities in user instruction or interpretation of these instructions in the process of generating actions for a robot. In this extended abstract, we summarize recent work on programming robots through natural language, identify a key research gap, and propose directions for future work with the aim of resolving ambiguities to robustly interpret and clarify natural language instructions.
Submission Number: 40
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