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

The release of nuPlan marks a new era in vehicle motion planning research,
offering the first large-scale real-world dataset and evaluation schemes
requiring both precise short-term planning and long-horizon ego-forecasting.
Existing systems struggle to simultaneously meet both requirements. Indeed, we
find that these tasks are fundamentally misaligned and should be addressed
independently. We further assess the current state of closed-loop planning in
the field, revealing the limitations of learning-based methods in complex
real-world scenarios and the value of simple rule-based priors such as
centerline selection through lane graph search algorithms. More surprisingly,
for the open-loop sub-task, we observe that the best results are achieved when
using only this centerline as scene context (\ie, ignoring all information
regarding the map and other agents). Combining these insights, we propose an
extremely simple and efficient planner which outperforms an extensive set of
competitors, winning the nuPlan planning challenge 2023.

nuPlan 提供了第一个大规模实际数据集和评估计划，对于需要精确的短期规划和长期本体预测，现有系统难以同时满足这两个要求，我们提出了一种基于规则的简单而高效的策划方法，从而在 nuPlan 计划挑战中赢得了胜利。