TR-Graph: Task Command Repair for Safe LLM-Controlled Robot Systems

We propose TR-Graph, a graph-structured framework for transforming defective task commands into safe and executable alternatives that maintain fidelity to the original command. TR-Graph intercepts robot task commands, builds a task repair graph linking grounded scene entities, task variables, and violation pathways, and generates repaired candidates guided by six formally defined repair operators. We further introduce a graph-conditioned contextual bandit that learns and adaptively prioritizes repair operators based on the graph structure of each defective command. Repaired commands are further ranked using a scoring objective that balances task fidelity against intervention cost, favoring repairs that minimize unnecessary deviation from the original command. We evaluate TR-Graph on external benchmarks, real-world robot experiments, and qualitative mechanistic analysis. Across evaluations, TR-Graph produced safer and more executable repairs than all baselines.