Large Language Models Enable Automated Formative Feedback in Human-Robot Interaction Tasks

TL;DR

This paper demonstrates how large language models can be combined with formal analysis to provide accessible, relevant, and actionable feedback in human-robot interaction tasks, enhancing learning and performance.

Contribution

It introduces a novel approach that integrates formal task assessments with LLMs to generate understandable feedback for non-expert users in HRI tasks.

Findings

LLMs can generate clear explanations of complex task assessments.

The integrated system provides tailored recommendations for learners.

The approach improves the interpretability of formal analysis results.

Abstract

We claim that LLMs can be paired with formal analysis methods to provide accessible, relevant feedback for HRI tasks. While logic specifications are useful for defining and assessing a task, these representations are not easily interpreted by non-experts. Luckily, LLMs are adept at generating easy-to-understand text that explains difficult concepts. By integrating task assessment outcomes and other contextual information into an LLM prompt, we can effectively synthesize a useful set of recommendations for the learner to improve their performance.