Neurosymbolic Explanation Selection in Robotics: Combining the Strengths of Planning and Foundation Models for XAI

Abstract

Robots operating in human environments should be able to answer diverse, explanation-seeking questions about their past behavior. We present a neurosymbolic pipeline that links a task planner with a unified logging interface, which attaches heterogeneous XAI artifacts (e.g., visual heatmaps, navigation feedback) to individual plan steps. Given a natural language question, a large language model selects the most relevant actions and consolidates the associated logs into a multimodal explanation. In an offline evaluation on 180 questions across six plans in two domains, we show that an LLM-based question matcher retrieves relevant plan steps accurately (F1 Score of 0.91), outperforming a lower-compute embedding baseline (0.62) and a rule-based syntax/keyword matcher (0.02). A preliminary user study (N=30) suggests that users prefer the LLM-consolidated explanations over raw logs and planner-only explanations.