LLM-Assisted Planning Failure Resolution in Human-Robot Collaboration

Abstract

In Human-Robot Collaboration, failures that are not explained or handled with alternative strategies can undermine user trust and interaction quality. Despite recent advances in LLM-based task planning, failures are often treated as terminal events, leading to non-transparent robot behaviors. This work investigates how explanatory and cooperative responses to planning failure impact human-robot collaboration. We propose a modular architecture that integrates symbolic planning in PDDL with LLM-based reasoning to diagnose planning failures, generate recovery strategies, and negotiate solutions with the human partner instead of halting the execution of the task and terminating the cooperation. We conducted a Human-Robot Interaction video-based study with 33 participants to investigate how different robot behaviors across controlled failure scenarios affect the human's perception of the robot. Results show that transparent explanations and cooperative negotiation significantly improve perceived intelligence and likability, while unexplained errors are rated most negatively. Our findings highlight that, in collaborative settings, managing interaction during failure is a key factor for effective human-robot collaboration.