Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Planners

TL;DR

This paper introduces KnowLoop, a framework for closed-loop LLM-based planning in robotics, utilizing an uncertainty-based failure detector to improve task success rates by filtering uncertain predictions.

Contribution

The work proposes a general-purpose uncertainty quantification method for failure detection in LLM/MLLM planning, evaluated with new metrics and a custom dataset.

Findings

Token probability and entropy effectively reflect uncertainty.

Filtering uncertain predictions improves failure detection accuracy.

Enhanced failure detection boosts task success in robotic planning.

Abstract

Recently, Large Language Models (LLMs) have witnessed remarkable performance as zero-shot task planners for robotic manipulation tasks. However, the open-loop nature of previous works makes LLM-based planning error-prone and fragile. On the other hand, failure detection approaches for closed-loop planning are often limited by task-specific heuristics or following an unrealistic assumption that the prediction is trustworthy all the time. As a general-purpose reasoning machine, LLMs or Multimodal Large Language Models (MLLMs) are promising for detecting failures. However, However, the appropriateness of the aforementioned assumption diminishes due to the notorious hullucination problem. In this work, we attempt to mitigate these issues by introducing a framework for closed-loop LLM-based planning called KnowLoop, backed by an uncertainty-based MLLMs failure detector, which is agnostic to any used MLLMs or LLMs. Specifically, we evaluate three different ways for quantifying the uncertainty of MLLMs, namely token probability, entropy, and self-explained confidence as primary metrics based on three carefully designed representative prompting strategies. With a self-collected dataset including various manipulation tasks and an LLM-based robot system, our experiments demonstrate that token probability and entropy are more reflective compared to self-explained confidence. By setting an appropriate threshold to filter out uncertain predictions and seek human help actively, the accuracy of failure detection can be significantly enhanced. This improvement boosts the effectiveness of closed-loop planning and the overall success rate of tasks.