  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.
