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.

通过引入一个名为 KnowLoop 的基于不确定性的 MLLMs 故障检测器支持的闭环 LLM-based planning 框架，我们试图减轻 LLM-based planning 的开放环路问题，进而提高任务的成功率。

闭环 LLM 规划器的基于不确定性的故障检测评估

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

There emerges a promising trend of using large language models (LLMs) to
generate code-like plans for complex inference tasks such as visual reasoning.
This paradigm, known as LLM-based planning, provides flexibility in problem
solving and endows better interpretability. However, current research is mostly
limited to basic scenarios of simple questions that can be straightforward
answered in a few inference steps. Planning for the more challenging multi-hop
visual reasoning tasks remains under-explored. Specifically, under multi-hop
reasoning situations, the trade-off between accuracy and the complexity of
plan-searching becomes prominent. The prevailing algorithms either address the
efficiency issue by employing the fast one-stop generation or adopt a complex
iterative generation method to improve accuracy. Both fail to balance the need
for efficiency and performance. Drawing inspiration from the dual system of
cognition in the human brain, the fast and the slow think processes, we propose
a hierarchical plan-searching algorithm that integrates the one-stop reasoning
(fast) and the Tree-of-thought (slow). Our approach succeeds in performance
while significantly saving inference steps. Moreover, we repurpose the PTR and
the CLEVER datasets, developing a systematic framework for evaluating the
performance and efficiency of LLMs-based plan-search algorithms under reasoning
tasks at different levels of difficulty. Extensive experiments demonstrate the
superiority of our proposed algorithm in terms of performance and efficiency.
The dataset and code will be release soon.

利用大型语言模型（LLM）生成类似代码的计划来进行复杂推理任务，提出一种集成单次推理和思维树的层次化计划搜索算法，平衡效率和性能需求，在多跳视觉推理任务方面表现出优越性能和效率。