A Simple Baseline for Uncertainty-Aware Language-Oriented Task Planner for Embodied Agents

Our research presents an improvement to task planning using Large Language Models (LLMs) by incorporating a simple approach to consider uncertainty in planning. This strategy, which differs from standard LLM-based planners, emphasizes quantifying uncertainty and exploring alternative paths for task execution. By establishing a method to measure uncertainty by setting appropriate thresholds on probabilities in skill selection, our planner is more capable at selecting a better path for carrying out tasks. Through our experiments in high-level planning within the ALFRED task domain, we observed an improvement in plan execution success rates by 0.96–2.41 percent points over conventional LLM-based task planners. These results demonstrate that uncertainty-aware strategies can lead to more precise and effective task planning.