Towards Reliable LLM-based Robots Planning via Combined Uncertainty Estimation
Authors: Shiyuan Yin
Chenjia Bai
Zhang Zihao
Junwei Jin
Xinxin Zhang
Chi Zhang
Xuelong Li
Venue: NeurIPS 2025 poster ()
Keywords: Language-based planning
Uncertainty Estimation
Uncertainty Quantification
Foundation Models for Decision Making
Primary area: deep_learning
TL;DR: We propose ​​CURE​​, a method that splits LLM planning uncertainty into ​​epistemic​​ (semantic-ambiguity/task-familiarity) and ​​intrinsic​​ parts for more reliable robot decision-making.

Abstract: Large language models (LLMs) demonstrate advanced reasoning abilities, enabling robots to understand natural language instructions and generate high-level plans with appropriate grounding. However, LLM hallucinations present a significant challenge, often leading to overconfident yet potentially misaligned or unsafe plans. While researchers have explored uncertainty estimation to improve the reliability of LLM-based planning, existing studies have not sufficiently differentiated between epistemic and intrinsic uncertainty, limiting the effectiveness of uncertainty estimation.
In this paper, we present Combined Uncertainty estimation for Reliable Embodied planning (CURE), which decomposes the uncertainty into epistemic and intrinsic uncertainty, each estimated separately. Furthermore, epistemic uncertainty is subdivided into task clarity and task familiarity for more accurate evaluation. The overall uncertainty assessments are obtained using random network distillation and multi-layer perceptron regression heads driven by LLM features. 
We validated our approach in two distinct experimental settings: kitchen manipulation and tabletop rearrangement experiments. The results show that, compared to existing methods, our approach yields uncertainty estimates that are more closely aligned with the actual execution outcomes. The code is at https://github.com/Firesuiry/CURE.

Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation
Shiyuan Yin', Chenjia Bai?, Zihao Zhang', Junwei Jin', Xinxin Zhang',
Chi Zhang": Xuelong Li
School of Artificial Intelligence, Henan University of Technology Institute of Artificial Intelligence (TeleAI), China Telecom
Abstract
Large language models (LLMs) demonstrate advanced reasoning abilities, enabling robots to understand natural language instructions and generate high-level plans with appropriate grounding. However, LLM hallucinations present significant challenge, often leading to overconfident yet potentially misaligned or unsafe plans. While researchers have explored uncertainty estimation improve the reliability of LLM-based planning, existing studies have not sufficiently differentiated between epistemic and intrinsic uncertainty, limiting the effectiveness of uncertainty esti- mation. In this paper, we present Combined Uncertainty estimation for Reliable Embodied planning (CURE), which decomposes the uncertainty into epistemic and intrinsic uncertainty, each estimated separately. Furthermore, epistemic uncertainty is subdivided into task clarity and task familiarity for more accurate evaluation. The overall uncertainty assessments are obtained using random network distillation and multi-layer perceptron regression heads driven by L.LM features. We validated our approach in two distinct experimental settings: kitchen manipulation and tabletop rearrangement experiments. The results show that, compared existing methods, our approach yields uncertainty estimates that are more closely aligned with the actual execution outcomes. The code at http:://ggtubb.coo/ieesrrrr////
Introduction
Large Language Models (LLMs) have shown exceptional versatility 2 3) evidenced by their ability to handle a wide array of tasks. These tasks include answering complex questions [4], solving mathematical problems [5) generating computer code (). and performing sophisticated reasoning during inference 77]. When robots try to execute tasks instructed by language descriptions, they can leverage LLM capabilities to interpret task instructions in natural language [8) 91 apply the common sense reasoning of LLMs to understand their environment [0], and formulate high-level action plans based on the robot's abilities and available resources.
However, a significant challenge with current LLMs is their propensity to hallucinate 1 i..., to confidently generate outputs that appear plausible but actually incorrect and practically in- feasible. [2]. This misplaced confidence in erroneous outputs presents a considerable obstacle to L.M--based planning in roboties. Additionally, natural language instructions in real-world environ- ments often contain inherent or unintentional ambiguity from human instructors 133] And following flawed plan with excessive confidence may lead undesirable or even unsafe actions. Consequently, offering an uncertainty estimation for the planning model prior to execution is desirable to reflect the level of confidence in their plans, allowing for halting execution or seeking human assistance in cases of high uncertainty.
"Coresponneence to Chenjia Bai chancc@Cchinatcleco..co
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Previous studies on uncertainty in reinforcement learning (RL) typically distinguish two components of model uncertainty: epistemic uncertainty and intrinsic uncertainty [14]. However, in research on robotics LLM planners, these uncertainty components have not been analyzed at fine-grained level. This lack of comprehensive analysis can lead to inaccurate assessments of model capabilities, such as misattributing uncertainty caused by environmental randomness to the model itself, thereby underes- timating its true abilities [15]. Furthermore, when environmental changes occur, it is challenging to isolate and update intrinsic uncertainty, hindering rapid adaptation to new conditions.
The empirical evidence from cognitive research reveals that clear task descriptions significantly enhance decision accuracy by providing precise framework for cognitive processing and reducing the cognitive load associated with ambiguity [16]. Conversely, vague objectives tend to introduce substantial cognitive dissonance, thereby increasing hesitation and the likelihood of errors [[7]. Similarly, the familiarity of tasks plays crucial role in execution efficiency. Familiar tasks, which have been ingrained through repeated exposure and practice, allow for streamlined cognitive and motor processes, thereby optimizing performance [[8] In contrast, unfamiliar tasks necessitate additional cognitive resources for schema construction and adaptation, thus introducing greater uncertainty and potential inefficiencies. These cognitive insights indicate that epistemic uncertainty can be decomposed into components related to task description clarity and task familiarity, enabling more precise uncertainty modeling for LLM task planning.
In this paper, we separately estimate plamning uncertainty and introduce new method for uncertainty estimation. First, we examine epistemic uncertainty in planning tasks, further breaking it down into two factors: task clarity and task familiarity. Second, we model intrinsic uncertainty as the expected success rate of given plan. A lower expected success rate indicates a higher level of intrinsic uncertainty as it implies that despite following the plan, there still exist factors within the environment that may lead to failure. To evaluate these uncertainties, we use multi-layer perceptrons regression heads driven by LLM features to estimate task clarity and expected success rate, while Random Network Distillation (RND) network is employed to assess task familiarity. addition, we propose a slower yet more precise task clarity evaluation method based on LLM-query. By combining these components, we provide a comprehensive uncertainty assessment for LLMe- planning, enhancing the accuracy of uncertainty estimation.
We compare the proposed algorithm with state-of-the-ar uncertainty estimation techniques robot planning, as well as widely-used uncertainty estimation methods in LLMs. The experiments include mobile manipulator in kitchen tasks and tabletop rearrangement tasks. The results demonstrate that the uncertainty estimates produced by the proposed method were the most accurate and comprehensive, showing the strongest correlation with the actual execution result.
First, we propose a novel uncertainty evaluation method CURE that separately assesses epistemic uncertainty and intrinsic uncertainty to better align model confidence with task success. Second, we present an effective method for assessing task similarity in task planning. leveraging RND to enhance the foundation for uncertainty estimation. Third, our experiments with mobile manipulator in kitchen and tabletop rearrangement scenarios demonstrate that the proposed algorithm significantly enhances the accuracy of uncertain estimation, outperforming existing methods in embodied planning and LLM uncertain estimation.
Our main contributions are summarized as follows:
Preliminaries
In this section, we introduce the core concepts underlying our proposed CURE framework, including the definitions of uncertainty, the problem formulation and an overview of RND.
Definitions of uncertainty. Epistemic uncertainty is uncertainty due to lack of knowledge or information. It is reducible in principle, as it can be decreased by gathering more data or building better models. It stems from ignorance. Intrinsic uncertainty (also called aleatoric uncertainty) is the inherent randomness or variation in a system itself. is irreducible, meaning it cannot be eliminated even with perfect information or infinite data. It stems from the fundamental chaosof the world.
Problem statement. We consider a manipulation problem which a high-level, free-form human instruction / is given (e.g., Give me something to drink). The observation O includes an instruction pertaining to the current environment, An L.MM planner decomposes the task into an object-centric executable task plan A. The central problem investigated in this work is how generate uncertainty

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Robot Planning
Uncertainty Estimation with CURE
Epistemic Uncertainty
Environment Context
Similarity Detection(sim) 0.2 is familiar)
Scene: Coke, Sprite, Apple
Human
Task Clerity Detection(amb) 0.8(instruction vague)
Give something to drink.
Koca-oll
Spriii
Intrinsic Uncertainty
LLM planner
will give user coke.
Expected Success Rate Detection(sue)
0.9g1 can do
Uncer tainty Result
0.732-1-suc 111-0 6"amo))) (High task uncertainty)
Figure Overview of the proposed uncertainty-aware LLM planning framework. Given natural language instruction (e.g.. Give me sth to drink) and environmental context (e.g.. Coke, Sprite, Apple), the LLM planner generates high-level plan (e.g.. will give user Coke). Our framework estimates the overall planning uncertainty using CURE module, which decomposes uncertainty into epistemic and intrinsic components. Epistemic uncertainty encompasses task similarity and task clarity, while intrinsic uncertainty is represented by the expected success rate of the generated plan. The final uncertainty score then guides the decision to proceed, halt, or request clarification, thereby enhancing planning reliability in uncertain ambiguous scenarios.
estimate U that quantifies the planner's confidence in the task planning process. For the convenience in subsequent processing, we input 1, O and A into the LLama3.2-8D model to obtain the hidden activations of the last token from the last layer, which serve as the task encoding vector T.
Let S {s1, . } represent the set of observed success rates, and U = {u,, ....... Un denote the corresponding set of planning uncertainty estimates, where is the number of samples.
The objective is to maximize the statistical dependence between and U, as measured by Spearman's rank correlation coefficient, p. Formally, we aim to solve:
max P(S, U)
(1)
where p(S.U) is defined as:
U) = =1 6 -. Lf n(n² I)'
d, == rank(s:) rank(u,).
(2)
Here, rank(s;) and rank(u,) denote the ranks of s; and u, within their respective datasets.
Random Network Distillation. Random Network Distillation (RND) is an approach initially utilized to measure the novelty of a given state, aiming to encourage exploration for rarely-visited states in large state space in RI. The core idea behind RND is to use randomly initialized neural network, referred to as the target network, which is kept fixed during training. separate predictor network is then trained to predict the output of the target network, typically via regression loss. The target network's weights are not updated throughout the training process, and its outputs remain fixed. This fixed target network is essentially source of "random" or "unknown" knowledge, and the predictor network attempts to minimize the difference between its predictions and the outputs of the target network. The error in the predictor network's 's predictions provides a measure of how well the predictor is able to capture the structure of the environment. High prediction errors typically indicate regions in the environment that are novel unfamiliar the agent, and low prediction emror signify regions that are more familiar, as the predictor network has learned to approximate the target network's behavior well. Formally, let denote the target network with parameters 0, and let Is represent the predictor network with parameters ó. The objective is to minimize the following loss:
((Ø) Exax (119(()) fe(
3

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where A' represents the input space (typically the state observation space of the task). The error between (x(z) and fa( reflects how well the predictor network approximates the fixed target network. In environments where the target network is highly unpredictable, the prediction error will be large, indicating the currently visited states are not familiar to the historical states. Utilizing this characteristic, we can assess the familiarity of tasks.
3 CURE Method
Sections and 3.2 provide a detailed description of the CURE architecture and our approach to estimating task familiarity. Next, Section 3.3 delves into the evaluation of task clarity and the prediction of the success rate.
3.1 Method Overview
We propose an overview of our method, CURE, which operates independently of any specific LLM planner. Additionally, this approach is crafted to be plug-and-play, meaning it requires no alterations to the foundational structures of the planners. After task planning is completed, we separately compute epistemic uncertainty and intrinsic uncertainty associated with the planning process. Epistemic uncertainty is further divided into task familiarity and task clarity. First, we employ RND estimate task familiarity, yielding Asim Next, task clarity is assessed using two proposed methods: (i) A query-based approach leveraging the LLM, which is relatively slower. (ii) A multi-layer neural network inference approach, which is computationally efficient. Both methods yield measure of task clarity, denoted as Aumb Finally, intrinsic uncertainty is computed using multi-layer neural network infer the expected success rate p. The final uncertainty U is determined using the following formula:
U 0n (1 11-02 02 Aamb) + Or Asim
where 01, a22 are tunable parameters that balance the contributions of task clarity, expected task success rate, and task similarity to overall uncertainty.
3.2 Task Familiarity Assessment
Firstly, we define the two key components in the RND framework: the target network and the predictor network. The outputs of the target network furget and the predictor network frred the embedding vectors Zearget and Zpred, respectively. Specifically:
Zurget
=furge (T),
Zrred
Spred (T).
where denotes the feature vector of the task description, as detailed in Section
During the training phase of the task, we train the RND network by inputting the vectors of known tasks, employing the Mean Squared Error (MSE) as the loss function to optimize the output discrepancy between the target network and the predictor network:
Known lask
Llama encoder
embeedings
New task
New task
Infer
Infer
embedings
Train
Task familianty new task
Target network
Target network
Trained predict network
Origin predict network
LMSE
Σ
Miagge
Zeread
(6)
D
(TDD
Figure 2: The process of task familiarity assess- ment
where D is the dataset of known tasks.
During the inference phase, the new task description vector T is input into the trained RND network. By computing the Euclidean distance between the outputs of the target network and the predictor network, we obtain the contextual similarity metrie Asim:
Asim
Zuurget
Zypred 2-
This similarity metric reflects the degree similarity between the current task and the known tasks. A larger value of Asm indicates greater difference between the current task and known tasks,

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suggesting that the task is relatively unfamiliar, thereby appropriately increasing the uncertainty in the output. Conversely, a higher similarity indicates that the current task is more familiar, allowing for reduction in the uncertainty assessment.
3.3 Assessment of Task Clarity and Expected Success Rate
Slow Evaluation of Task Clarity Using LLM (Ambiguity) We employed vanilla approach to assess task clarity by designing dedicated prompt for ambiguity evaluation. This prompt requires the model to determine whether the given task description provides sufficient information infer the intended object and location of the user's intent. The specific prompt can be seen in Appendix C
Through this prompt, prompt the model to determine whether the task description is sufficiently clear to infer the user's intended object and location. If the model deems the task description inadequate for inference (choose multi item or target location), we consider the task to exhibit semantic ambiguity, thereby increasing the uncertainty of the output. Conversely, if the task description is sufficiently clear (choose one item and one target location), the model's response indicates that the task instructions are adequate, thereby reducing uncertainty.
The final assessment result is obtained as follows:
{
Sufficient (Information Complete) Insufficient (Ambiguity Present)
Aamb
(8)
Fast Evaluation of Task Clarity and Expected Success Rate Using Uncertainty Assessment Network (UAN) First, we construct a training dataset using either code generation or LLM-ggeneratd data. This dataset comprises the task encoding vector T, the presence of task ambiguity Aumb E (0, } and whether the planned subtasks were successfully executed y E {0.
We design multilayer neural network fUAN. which takes input the feature vector T and outputs two scalar values:
Task
1000 MLP
encoder
Clarity
Scence
1. Task clarity score Aamh E [0, 1]
Train dataset Part1:
ambiguous task
10
Expected
2. Expected task success rate [0, 1]
Success Rate
Train dataset Part2:
clear task
The network training process employs cross- entropy loss function:
Figure The process UAN. During the training process, Llama consistently remained frozen. For tasks with clear objectives, both task clarity and expected success rate were trained. In tasks with ambiguous goals, only task clarity was trained.
Liotal Lamb + T((Aumb = 0) Loscceess
where II(-) an indicator function, and the loss terms are defined as follows:
Lamb = -(Aamm log( + (1 - -Aumb) log(1 Aamb))
(10)
Cunceess
log(() + y) log(1 D))
(11)
4
Related Work
LLMs as Planners. Emergent reasoning enables Large Language Models (LLMs) to decompose
tasks into intermediate sub-goals and generate sequence planned actions 19 Through prompting
and in-context learning, LLMs can translate natural language human instructions into executable robotic actions based on scene descriptions [1]. RoboCodeX [22]. ProgPrompt 23 and CaP
24
decomposes human instructions and leverages code generation to generate actions. RoboMamba 25 adopts Mamba-based end-to-end rision-languagg-action (VLA) model for robotic manipulation. RoboMP (26] method enhanced robotic manipulation by integrating multimodal perception and planning improve generalization and decision-making capabilities. Recent studies further enhance reasoning and planning capabilities by iteratively refining actions via self-reflection during the
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Tool-Planner [30], ReAct [3] and Reflexion [32 focus on multi- step planning scenarios, where a robot can execute certain actions, observe state feedback, and re-plan to make corrections. Tree-Planner 3 introduces tree sampling based solution, wheh improvements in error reduction. However, safety-critical robotic applications, certain invalid actions may lead to irrecoverable and catastrophic safety failures. Therefore, our approach aims to provide an uncertainty estimate after planning, enabling the system to withhold execution of plans with insufficient confidence by anticipating uncertainty before action execution.
planning process [27 28, 29).
Quantifying Uncertainty in LLMs. LLMs often experience hallucinations 34 which present two major challenges in planning. First, they might struggle to assess if a plan is achievable given specific problem description [6]. Second, they can generate inadmissible actions and non- non-existent objects, requiring translation or expert intervention to correct them [9 37.. To solve hallucinations, the natural language processing community has shown increasing interest in quantifying uncertainty in the outputs of large language models (LI.Ms) [3 39 40 41), calibrating this uncertainty empirically for aceuracy have also focused on quantifying uncertainty in LLMs within the domain of robotic task planning. For instance, the KnowNo framework [47) treats task-level planning as a multiple-choice question answering (MCQA) problem, and uses conform prediction to output subset of candidate plans generated by the LLM. IntroPlan [48), aligns the robot's uncertainty with the intrinsic ambiguity of task specifications before predicting high sighhhonfidence subset of plans. However, these studies primarily focus on the success rate of the overall task, with less attention given to the quality of uncertainty estimation, and the distinct sources of uncertainty were overlooked. This paper presents a supervised learning method using neural network outputs to estimate uncertainty, distinguishing between epistemic uncertainty and intrinsic uncertainty. This approach offers more comprehensive understanding and capture of uncertainty in task planning. It provides a more accurate and faster estimation of task planning uncertainty compared to existing methods.
and examining the reliability of models 45 46). Some researchers
5 Experiments and Result
We evaluate our methods across a diverse range of language-instructed tasks and environments, demonstrating their effectiveness in generating uncertainty assessments that are strongly correlated with success rates.
Baselines. We consider 8 baselines, including KnowNo 47 IntroPlan
48 and several uncertainty
estimation approaches in large language models (LLMs)) 38 KnowNo
47 treats task-level planning as a multiple-choice question answering (MCQA) problem and employs conformal prediction to output a subset of candidate plans generated by the LLM. Since there is no uncertainty output value, the softmax probability of the best option is directly used as the uncertainty estimate for task planning. IntroPlan [4]] introduces a knowledge base search process, incorporating the retrieved knowledge into the context when estimating uncertainty to enhance prediction accuracy. Multi- step 38 decomposes the reasoning process into steps and extracts confidence levels for each step, which can help mitigate overconfidence. Top-k (38) prompts the model to output multiple task plans and their respective confidence levels. CoT 38 uses zero-shot chain-of-though (CoT), which has been proven effective in inducing reasoning processes and improving model accuracy across various datasets. Vanilla (38) directly instructs the L.LM to state its confidence. Self-prohing 38 first generates answer and then retrieves the confidence expressed verbally in another independent chat session. Self-probing-log is similar to self-probing but applies softmax to the probabilities of outputting "yes" and "no," using the probability of "yes" as the LLM's confidence.
Proposed Methods (Specific introduction provided in Section 3.3 and Appendix(). Ambiguity An inquiry is made into whether the directive to the LLM is ambiguous. If it is ambiguous, the confidence in the question is assessed as O; otherwise, it is assessed as 1. CURE-Ambiguity The confidence value is derived based on the value of ambiguity, in conjunction with the output of CURE. KnowNo-Ambbguit The confidence value is assessed based on the value of ambiguity, in combination with the output of KnowNo methodologies. CURE w/o sim Confidence is solely derived from the output of the UAN network. CURE The confidence value is derived from Equation (15).
Metries. We introduces novel metric, termed SR-HR-AUC, to more fairly assess the performance of uncertainty estimation methods. The metric quantifies the accuracy of uncertainty estimation by

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analyzing the variation in the success rate (SR) of task at different help rates (HR). In addition to the SR-HR-AUC metric, we also utilize the Spearman's rank correlation coefficient evaluate the relationship between uncertainty estimation and task success rate. To further assess whether the correlation is significant, we compute the corresponding p-value. Both the Spearman coefficient and the associated p-value together provide a quantitative evaluation of whether a significant monotonic relationship exists between uncertainty estimation and task success rate, offering additional statistical support for the validity of the uncertainty estimation method. A detailed explanation of these metrics can be found in Appendix A
Implementation Details.
To facilitate reproducibility, we adopt open-source large language mod- els (LLMs) as the planner in our approach. For the kitchen operation experiments, we utilize Llama-3.2 3--.33.33773 1---70B-Isstuee to accomplish the tasks. In the case of the rearrangement experiments, which are relatively simpler, we employ Llama-3.. IeBB-Inttructt to complete the tasks. this paper, we set experiments for these parameters. All experiments were conducted on a computing server equipped with dual Intel Xcon Gold 6348 processors, 512GB of RAM, and four NVIDIA A100-PCIE-40GB GPUs. Conducting the experiment took approximately 12 hours.
1, o2 0.6, and = 30. Appendix F contains the hyperparameter search
5.1 Mobile Manipulator in a Kitchen
Table I: Results for Mobile Manipulator in a Kitchen
experiment name
spearman
p-value
SR-HR-AUC
introplan
0.030
0.599 929
0.005
self-probing-log
0.181
0.001 651
0.094
self-probing
0.111
0.054 750
0.120
vanilla
0.221
0.000 115
0.236
cot
0.247
1.467 10
0.273
top-k
0.254
8.286 x 10-6
0.286
multi-step
0.247
1.520
10-5
0.297
KnowNo
0.336
2.441
10-9
0.395
Ambiguity(Ours)
0.433
3.802 x 10
0.371
CURE w/o sim(Ours)
0.417
4.431
10
0.483
KnowNo-Ambiiguix(OOrs
0.426
1.114 x 10
0.501
CURE(Ours)
0.454
1.079
10-16
0.534
CURE-Ambiguity(Ouss
0.466
1.460
10-17
0.547
For this environment, we adopt the task specifications defined in KnowNo (77 Each scenario involves mobile robot positioned in front of kitchen counter, adjacent to a set of recyclin/comppostrrall bins. The task entails picking up objects from the counter and placing them either into one of the bins or elsewhere the counter. The environment exhibits certain ambiguities, including scenarios involving potentially unsafe actions.
The results presented in Table a comprehensive evaluation of various uncertainty estimation methodologies applied to the mobile manipulator task in a kitchen environment.
The baseline methods demonstrate varying degrees of effectiveness in estimating uncertainty and predicting task success, as evidenced by both Spearman correlation and SR-HR-AUC metrics. IntroPlan shows a relatively low Spearman correlation of 0.030 (p = 0.60) and the lowest SR- HR-AUC value of 0.005, possibly due to its high initial success rate. Self-probing-log achieves Spearman coefficient of 0.181 (p 0.0017) and an SR-HR-AUC value of 0.094. The vanilla approach yields a higher correlation of 0.221 (p = 0.0001 15) and an HR-HR-AUC value of 0.236. CoT performs slightly better, with Spearman coefficient of 0.247 (p= 1.467e-5) and an SR-HR-AUG value of 0.273. 0.273.Top-k and Multi-step methods exhibit the highest correlations among the baseline methods-0.25 0.25 and 0.247, respectively and corresponding SR-HR-AUC values of 0.286 and 0.297. Notably, KnowNo outperforms the other baselines with correlation coefficient of 0.336 (p= 2.441c-9) and an SR-HR-AUC value of 0.395, highlighting its superior performance in this context.
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The proposed methods significantly outperform the baseline approaches. The Ambiguity method achieves a Spearman correlation of 0.433 (p-value: 3.802e-15) confirming the importance of ambi- guity recognition in uncertainty estimation. The CURE w/o sim and KnowNo- Ambiguity methods further enhance performance, with correlations of 0.417 and 0.426, respectively, both showing highly significant p-values. The CURE method achieves an even higher correlation of 0.455 (p-value: 1.079e- 16), demonstrating the benefits of integrating UAN and RND approaches. The CURE-Amin Ambbe-iy method emerges the most effective, achieving the highest Spearman correlation of 0.466 (p-value: 1.460e-17) and the best SR-HR-AUC value of 0.547.
The help-success curves of these methods are shown in Fand It can also be observed from the figures that the curve of the proposed method has a larger area. As observed from Figure the confidence interval of the proposed method notably narrow, indicating that the performance of CURE is highly stable.
DURE
0.9
St0000 Sato
0.7
In conclusion, the proposed methods consis- tently outperform the baseline approaches, pro- viding more accurate and reliable uncertainty estimates in the kitchen operation task. These results validate the novel methodologies and metrics introduced in this study, offering sig- nificant advancements in uncertainty estimation for L.M--istrecetd tasks.
0.3
0.2
Figure 4: the Help Rate-Success Rate Curve of CURE and KnowNo for Mobile Manipulator in a Kitchen two variants of the CURE algorithm were executed 11 times. The lighter-colored region represents the 2a confidence interval.
5.1.1 Result for CURE+IntroPlan
We conducted additional experiments to evalu- ate the Overstep Rate, Overask Rate, and Help Rate of the IntroPlan method, CURE method, and IntroPlan + CURE method when the target success rate is 90%. The experimental results shown in Table 2
Table 2: Comparison of Overstep Rate, Help Rate and Overask Rate for CURE and IntroPlan.
Metric
CURE
IntroPlan CURE
IntroPlan
Overstep Rate +
34.88%
21.58%
31.82%
Help Rate J
71.33%
53.67%
19.33%
Overask Rate 4.
29.44% %
53.42%
51.72%
The results show that the IntroPlan CURE method achieved the absolute optimal effect in terms of Overstep Rate, significantly reducing unsafe incidents. In terms of Help Rate, IntroPlan demonstrated the lowest rate, indicating higher confidence and reduced need for human intervention. However, considering the small difference between IntroPlan + CURE and IntroPlan in the Overask Rate metric, suggests that the help behavior of IntroPlan + CURE was not ineffective, significantly improving safety performance. CURE performed best in the Overask Rate, partly due to the lower initial success rate its base method, KnowNo, making the effectiveness the help more significant.
5.2 Tabletop Rearrangement
In this task, the robot arm is required to rearrange objects a table within the PyBullet simulator. Each scene initialized with three bowls and three blocks, each of which is colored blue, green, and yellow, respectively. The task requires the robot to move specific number of blocks or bowls to designated position relative to another object. For example, "Move the green block to the left of the blue bowl."
In the tabletop rearrangement task, we use the task definitions from KnowNow and conduct ex- periments within the PyBullet simulation environment. The robot is required to parse ambiguous user instructions and perform the appropriate object rearrangement actions. The primary goal of the experiment is to evaluate the effectiveness of different uncertainty estimation methods in handling

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Table Results for Tabletop Rearrangement
experiment name
spearman
p-value
SR-HR-AUC
vanilla
0.328
6.519 x 10-B
0.293
0.312
3.901 x 10
0.320
self-probing
0.282
7.751 x 10-7
0.301
multi-step
0,161
5.222 x 10
0.192
top-k
0.155
7.281
10
0.146
KnowNo
0.302
9.609 X 10-8
0.351
CURE w/o sim(Ours)
0.610
6.347
10-32
0.703
CURE(OurS)
0.635
2.827 x 10-35
0.732
ambiguous instructions. Due to the simplicity of the experiment and the effectiveness of the fast methods, we did not test the slower methods(LLLM based Ambiguity method).
The results presented in Table offer a comparative analysis of the baseline and proposed methods in estimating uncertainty during object rearrangement tasks. Among baseline methods, the Vanilla approach achieves a Spearman correlation of 0.328 (p 6.519e-9), outperforming other baselines such CoT (0.312, p = 3.901e-8) and Self-probing (0.282, p p 7.751e-7). This suggests that direct confidence elicitation from LLMs can provide moderately reliable uncertainty estimates for simpler rearrangement tasks. However, decomposition based methods like Multi-step (Spearman = =0.161) and Top-k (Spearman 0. 155) exhibit notably weaker correlations, indicating that stepwise confidence aggregation may not generalize well to this domain. KnowNo, while achieving a competitive Spearman coefficient of 0.302 (p = =9. -9.609--),, remains limited compared to our proposed methods, highlighting inherent constraints KnowNo approaches handling instruction ambiguitics.
The proposed methods exhibit substantial improvements. CURE w/o sim achieves Spearman correlation of 0.610 (p- 6.347c-32), nearly doubling the performance of the best baseline. This demonstrates the effectiveness of the UAN in capturing task-specific uncertainties. The integration of RND CURE further enhances performance, yielding the highest Spearman coefficient of 0.635 (p 2.827e-35) and SR-HR-AUC of O. 0.732. The extreme statistical significance (p < le-30) of both methods underscores the robustness of their uncertainty estimates.
The SR-HR-AUC metric reveals practical implications: CURE's AUC of 0.732 indicates superior ability to modulate help requests based on uncertainty, compared to KnowNo's 0.351. This 108% improvement demonstrates that our methods more effectively align uncertainty estimates with actual task success probabilities, critical for safe human-robot collaboration.
To further visually demonstrate the performance of different methods, we plotted the help rate-success rate curves for the baseline method and the proposed method (Figures 77) As shown in the figures, the area under the curve for the CURE method is significantly larger than that of the baseline method, indicating its superior ability identify tasks with high uncertainty and take appropriate actions.
In summary, the proposed CURE methodologies markedly outperform the baseline methods, provid- ing more accurate and reliable uncertainty estimates in the tabletop rearrangement task.
Conclusion
This paper presents CURE, an innovative framework designed to enhance reliable planning within robotics applications through the utilization of L.MMs. CURE significantly improves the alignment between uncertainty estimates and actual execution outcomes. Empirical evaluations conducted on tasks involving kitchen manipulation and tabletop rearrangement demonstrate that CURE surpasses existing methodologies, offering uncertainty estimates that exhibit a stronger correlation with the rates of task success. Furthermore, CURE is characterized by its ease of integration with any LLM-based planner, thereby requiring minimal effort for implementation.
Limitations and Future Work. A primary limitation of our current work is that the predictive model necessitates pretraining on existing datasets, and the model lacks generalizability. requiring deployment tailored to specific task sets. Secondly, the current generation of uncertainty has not

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been calibrated with actual success rates, necessitating an additional calibration step to align with the confidence in execution.
Future research endeavors will focus on refining the CURE framework by integrating dynamic task familiarity models capable of real-time adaptation, thereby augmenting the framework's 's robustness across a broader array of increasingly complex task scenarios. Additionally, the incorporation of physical concepts into the uncertainty prediction model proposed as means to enhance the model's generalizability, allowing it to better accommodate a wider variety of tasks and conditions.
7 Broader Impact
In this article, we propose a fine- fine-graine grained method for uncertainty estimation by quantifying the inference confidence of foundational model through both the model's epistemic uncertainty and the intrinsic uncertainty of the environment. CURE operates as plug-and-pla approach, which requires no modification of existing workflows, and can be seamlessly integrated into current I.L.M-based robotic planners as supplement. Furthermore, we contend that CURE can serve as a general method, extending the inference capabilities of foundational models beyond robotic applications.
However, as previously mentioned, during the implementation of our model, our approach continues yield high uncertainty unseen environments. This maintains safety but necessitates new training to improve accuracy. This limitation in generalization performance restricts the applicability of CURE.
Acknowledgments and Disclosure of Funding This work is supported by the National Key Re- search and Development Program of China (Grant 20.2224YFE2110000) the National Natural Sci- ence Foundation of China (Grant No. No.62306242), the Young Elite Scientists Sponsorship Program by CAST (Grant No. 2024QNRC001), the Yangfan Project of the Shanghai (Grant No.23YF1 1462200) and the Key Scientific Research Project Plan for Higher Education Institutions in Henan Province (Grant No.26A520006..
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Does the paper describe the usage of LLMs if is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: This paper uses LLMs solely for language polishing, grammar correction, and translation.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our LLM policy (https://neurd) hutt:://eeerrpp.cc/oofffeeeeeeee /c/////oeeeeeeeee/222////.. for what should or should not be described.
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A Matrics Detail
A.1 SR-HR-AUC
SR-HR-AUC offers more equitable evaluation of the performance of uncertainty estimation methods. This metric quantifies the accuracy of uncertainty estimation by analyzing the variation in the success rate (SR) at different help rates (HR). Specifically, in given task, robot ensure the success of the task by requesting human assistance; if no assistance is requested, the task success rate is determined by the success rate of the planning model based Assume there are 100 tasks, each planned using an LLM, and uncertainty is evaluated using an uncertainty estimation module. At various help rates, tasks with the highest uncertainty selected for human intervention, while the remaining tasks are autonomously executed by the robot based on the LLM's plan, resulting in an overall success rate.
large language model (LLM).
At a help rate of O, the task success rate is y, which reflects the actual performance of the planning model; at help rate of I, the task success rate is necessarily 100%. By plotting the curve of help rate versus success rate, the area under the curve (AUC) can be used to assess the precision of uncertainty estimation. However, to eliminate the influence of the model's planning performance uncertainty estimation, this study introduces an additional normalization procedure.
Specifically, two benchmark curves are defined in this study:
1. Random Evaluation Curve: Assumes that the uncertainty of all tasks is a random value. This curve linearly ascends from the baseline success rate point (0. to (1, 1).
2. Perfect Uncertainty Evaluation Curve: Indicates strong correlation between uncertainty and task success rate. It ascends from (0, y) to (1 y, 1), then remains constant.
where y the task success rate when help rate is O.
By calculating the area difference between the actual uncertainty evaluation curve and the random evaluation curve, and dividing this by the area difference between the perfect evaluation curve and the random evaluation curve, a normalized AUC value is obtained. This normalization process effectively eliminates the influence of the baseline success rate, allowing SR-HR-AUC more accurately reflect the quality of uncertainty estimation.
As shown in the figure, the actual uncertainty evaluation curve (orange curve) lies between the random evaluation curve (purple dashed line) and the perfect evaluation curve (green solid line). By calculating the areas under each curve and applying the normalization formula described above, the resulting SR-HR-AUC value provides a reliable measure whether uncertainty estimation is closely related to task success rate, thus offering robust evaluation of uncertainty estimation methods.
Actual AUC Random AUC
SR-HR-AUC Normalized AUC
Perfect AUC Random AUC
A.2 Spearman's Rank Correlation Coefficient
Spearman's 's rank correlation coefficient is non non-parametric statistical method used to assess the monotonic relationship between two variables. Unlike Pearson's 's correlation coefficient, Spearman's 's coefficient does not require the data to follow normal distribution or exhibit a linear relationship. making it suitable for various data types. particularly when the data does not conform to linear relationship, thus demonstrating better robustness.
Specifically, the Spearman coefficient evaluates the relationship between variables by ranking them and then calculating the correlation between these ranks. Its value ranges from [11, where:
A value of 1 indicates a perfect positive correlation between the two variables (i.e., as one variable increases, the other variable monotonically increases);
A value of -1 indicates perfect negative correlation between the two variables (i.e., as one variable increases, the other variable monotonically decreases);
A value of 0 indicates monotonic relationship between the two variables.
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SR-HR-AUC Metric for Uncertainty Estimation
1-y
1.0
Success Rate (SR)
Perfect Estimation
Random Estimation
0.5
Actual bacértainty Estimation
y
Legend:
Actual Estimation
Random Estimation
Perfect Estimation
0
0.4
0.8
1.0
Help Rate (HR)
Figure 5: The SR-HR-AUC metric is used to evaluate the performance of uncertainty estimation. The figure shows three curves: the actual uncertainty evaluation curve (orange solid line), the random evaluation curve (purple dashed line), and the perfect uncertainty evaluation curve (green solid line). The horizontal axis represents the help rate (HR), and the vertical axis represents the success rate (SR). By calculating the area difference between the actual curve and the random curve, and dividing by the area difference between the perfect curve and the random curve, a normalized AUC value obtained. This metric effectively eliminates the influence of the baseline success rate, providing more accurate reflection of the quality of uncertainty estimation.
To further assess the significance of the correlation, compute the corresponding p-value. The p- value is used to measure the consistency between the observed result and the null hypothesis (usually "no correlation"). A small p-value (typically less than 0.05) indicates that the null hypothesis is rejected, suggesting that the correlation between the two variables is statistically significant, meaning there is significant monotonic relationship. Conversely, larger p-value indicates that the null hypothesis cannot be rejected, suggesting that the correlation is not significant.
Therefore, the Spearman coefficient, along with the corresponding p-value, provides quantitative evaluation whether significant monotonic relationship exists between uncertainty estimation and task success rate, offering further statistical support for the validity of uncertainty evaluation methods.
B Additional Results
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varilo
sef-probingg
zerii randor
cSt
sellprcing
Savcesse Sat*
saf probing
muti 5top
too
selpretery Agg
Will shop
tep-
<nowNoo
Ambigutty
CURE Ambigulty
KnowWoo-Aman Amolguity
introPton
Emvifón
0.7
0.
2.2
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04
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Hulp Pate
Help Patto
Figure 6: The Help Rate-Success Rate Curve for Mobile Manipulator in a Kitchen
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sef-probing
ranken
randon
randon
Sapcher Sare
sel-grobingiog
top-k
exFprsting randor
randon
randirn
Sacrase Sare
08-
Growlin
CURF w/o sim
CUB
Knosdo
CUSE
Pace
System Dode
0.0
22
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00
02
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Figure 7: The Help Rate Success Rate Curve For Tabletop Rearrangement
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B.1
Dataset scale result
We conducted experiments on dataset scale, and the results are as follows: The experimental results
Training Set Size
100
300
1000
3000
10000
30000
100000
SR-HR-AUC
0.125
0.146
0.174
0.234
0.362
0.430
0.547
Table 4: Experimental results on dataset scale.
demonstrate that increasing the dataset scale significantly improves the performance of the CURE model. When the dataset reaches about 10,000 samples, the performance improvement becomes noticeable, and further scaling up 100,000 samples still yields continuous gains.
B.2 Calibrate result
Our approach involved splitting the test set into two parts: one for calibration and the other for testing. Given target success rate, first determined threshold on the calibration Tasks with confidence below the threshold were completed with human assistance, while tasks with confidence above the threshold were executed autonomously. By adjusting the threshold, could make the success rate approach the target success rate, thereby achieving calibration.
We tested this method for a target success rate of 85%. The results showed that the success rate on the test set reached 84.33%, differing from the target by only 0.67%. This demonstrates that the CURE method achieves good calibration accuracy.
C Prompt Template
In the following prompt, "SCENE" refers to the objects present in the current environment, for example, [Coca-Cola, Sprite, apple]. "TASK" refers the current instruction, such [give something to drink].
C.1 Ambiguity method prompt
You are a robot in kitchen. You are given a scenario and task. You need to place an item in new location.
Scenario: "SCENE"
Task: "TASK"
Optional items are: "SCENE"
Optional target locations are: "SCENE", user's hand, top drawer, bottom drawer, garbage can. Please tell me what items you choose from and where is the target locations. You can choose one or more. You need to choose every item and target location fit the task. (Based common-sense reasoning, extreme special cases should be disregarded.)
C.2
Vanilla prompt
You are a robot operating in an office kitchen.
You are in front of counter with two closed drawers, a top one and bottom one. There is also a landfill bin, a recycling bin, and compost bin. On the counter, there is "SCENE"
"TASK"
provide what will you do and your confidence in this answer.
Note: The confidence indicates how likely you think what will you do is true.
Note: The action you take should be one of [pick-up, bring, move, open, close] and something you seen(should be single full name) [to where(should be single full name) you seen if needed].
Use the following format to answer:
"Actiion[Wrr [Wht will you do]
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Confidence: [Your confidence level, please only include the numerical number in the range of 0- 100]%"
Only the answer and confidence, don't give me the explanation. Now, please answer this question and provide your confidence level.
C.3
COT prompt
You robot operating in office kitchen.
You are in front of counter with two closed drawers, a top one and bottom one. There also landfill bim, a recycling bin, and compost bin.
On the counter, there is "SCENE"
"TASK"
analyze step by step, provide what will you do and your confidence in this answer.
Note: The confidence indicates how likely you think your action true. Use the following format to answer:
"Explanation: [insert step-by-step analysis here] Action:[What will you do Here]
Confidence: Your confidence level, please only include the numerical number the range of 100]%"
Note: The action you take should be one of (pick-up, bring, move, open, close] and something you seen(should be single full name) [to where(should be single full name) you seen if needed].
Only give me the reply according to this format, don't give me any other words. Now, please answer this question and provide your confidence level. Let's think it step by step.
C.4
Self probing prompt
You are robot operating in office kitchen.
You are in front of counter with two closed drawers, a top one and bottom one. There is also landfill bin, recycling bin, and compost bin. On the counter, there is "SCENE" "TASK"
What will you do?
Note: The action you take should be one of [pick-up, bring, move, open, close] and something you seen(should be a single full name) [to where(should be single full name) you seen if needed].
"LLM response"
According to your response, what is your confidence in this action? reply with the confidence only. the confidence should be a percentage.
C.5 Multi step prompt
You robot operating in office kitchen.
You are in front of a counter with two closed drawers, a top one and a bottom one. There is also landfill bin, recycling bin, and compost bin. On the counter, there is "SCENE" "TASK"
What will you do? only interact with the objects the scene.
Read the question, break down the problem into steps, think step by step. give your confidence in each step, and then derive your final answer and your confidence in this answer.
Note: The confidence indicates how likely you think your answer is true.
Use the following format to answer:
"Step 1: [Your reasoning]. Confidence: [ONLY the confidence value that this step is correct)... Step K: Your reasoning], Confidence: [ONLY the confidence value that this step correct]Final Answer and Overall Confidence (0-100): [ONLY the answer type; not complete sentence), [Your confidence value]%"
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C.6
Top-k prompt
You are robot operating in an office kitchen. You are in front of counter with two closed drawers, a top one and a bottom one. There also landfill bim, a recycling bin, and compost bin. On the counter, there is "SCENE" "TASK"
What will you do? only interact with the objects in the scene. Provide your k best guesses and the probability that each is correct (0% to 100%) for the following question.
Give ONLY the task output description of your guesses and probabilities, no other words or explanation.
Note: The action you take should be one of lpick-up, bring, move, open, close] and something you seen(should be single full name) [to where(should be single full name) you seen if needed]. For example:
G1: <ONLY the action description of first most likely guess; complete sentence, just the guess!>
P1: <ONLY the probability that G1 is correct, without any extra commentary whatsoever; just the probability!>
Gk: <ONLY the action description of k-th most likely guess>
Pk: <ONLY the probability that Gk is correct, without any extra commentary whatsoever; just the probability!>
D Additional implementation details
CURE-Ambiguity
U=1 (1 02 0.5 (Aamhl + Aamh2)) 0r Asm
(12)
Where Aambi and Aambe are derived from Ambiguity and UAN, respectivlly.. Ambiguity
U - 01 02 Aumbi) p az Asi
(13)
Where Ammbi is derived from Ambiguity. KnowNNo-Amiguity
--1-aa 10 0.5
Aambi) p CKnowNo
(14)
Where CKnowNo is the confidence from KnowNo and Aumbi is derived from Ambiguity. Complete Procedure for Kitchen Operation Experiment
First, we grasp the initial information including the scene and task, for example:
Scene: bottled unsweetened tea, an orange, and a bag jalapeno chips
Task: Put jalapeno chips in the drawer.
Next, we supplement the complete scene information to form complete scene description:
We: You are robot operating in an office kitchen. You are in front of a counter with two closec We: On the counter, there is orange soda, Pepsi, and an apple.
Based on this, we generate operation instructions according to the complete scene and task. This generation process is consistent with KnowNo[? J. emphasized that this generation method independent of the method proposed in this paper and can adopt any method to generate operation
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instructions. The generated operation instruction is (if the instruction is not in this format, it will be reformatted by LLM):
action: pick-up jalapeno chips to bottom drawer
Next, we combine the aforementioned content into prompt with specific content:
You human and there is robot operating in office kitchen. The robot is in front of On the counter, there is {scene}. You say: "[task}". Then the robot (action}.
We input this prompt into the Llama3-8B model and extract the last layer's hidden state Waatt_hiden__statee as the feature vector T.
Subsequently, the feature vector is input into the UAN network and RND network to obtain the final expected success rate p, ambiguity Aamb, and familiarity Asim:
p 0.962
Aamb 0.928
Asim 0.048
Finally, the uncertainty (Uncertainty) is calculated using Eq,4; Confidence calculated as: 1 U. The confidence value in the example is 0.378.
E Hyperparameter Search Experiments
In this section, we detail the hyperparameter search experiments conducted to fine-tune the parameters Q1 1, a2 0.6, and 03 30 in the uncertainty estimation formula, as shown in Equation [5] These experiments were performed within the context of the Mobile Manipulator in a Kitchen environment.
(15) 01 set and remained unchanged throughout the experiments. This decision made to simplify the equation's 's structure, ensuring that the primary scaling of the output confidence is directly influenced by the product of the other components. By maintaining ai at constant value, ensure that the adjustments confidence primarily reflect changes in ambiguity (Aumb) and similarity the interactions of these factors. The hyperparameter
U-1-ai (1-02 Aamh) + Or Asm.
(Aam), thereby focusing the exploration search aimed to optimize 02 and ag to balance the contributions of ambiguity and similarity to the overall uncertainty score. Several values were tested for each parameter to assess their impact on the performance metrics, particularly the Spearman correlation coefficient and SR-HR-AUC.
02 Search:
The parameter 02 modulates the influence of ambiguity on the uncertainty score. We
explored values ranging from 0.1 to 0.9 while keeping 03 = 30, with the corresponding SR-HR-AUC values depicted in Figure [ The experiments indicated that lower values resulted in diminished sensitivity to the ambiguity component, while higher values led to excessive influence, sometimes overshadowing the task success probability (p). The optimal value of a2 0.6 was chosen as it provided a balanced contribution to the uncertainty score, resulting improved correlation and IR-HR-AUC metrics.
The parameter 03 adjusts the weight of the similarity component. Values from 0 to 3000 were tested while maintaining a2 = 0.6, as shown in Figure Findings showed that lower values inadequately captured similarity nuances, whereas higher values introduced excessive noise into the estimation process. with the overall uncertainty, thereby maximizing the predictive performance as measured by the evaluation metrics. Overall, these hyperparameter settings were determined through exhaustive trials aimed at enhancing the model's ability to accurately estimate uncertainty and predict task success. The chosen values of a2 = 0.6 and 03 30 reflect the optimal balance between the distinct components of ambiguity and similarity, leveraging their individual strengths to achieve robust uncertainty estimation.
03 Search:
30 emerged as the optimal setting, aligning similarity's 's contribution
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alpho3 vs SR. ARC
alpha2 vs SR_HR ARUHRAUC
Jlp
0.30
0.45
0.35
SR 11
0.30
0.30
0.20
0.20
0.35
10r
107
10a
10a
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
alpha3
alsha2
Figure 8: SR-HR-AUC values for different settings of a2 and ag.
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Reviews:
[{"review_id": "6w5Qki4sBl", "reviewer": "Reviewer_9hBH", "summary": "The paper introduces CURE, a framework for estimating uncertainty of LLM-based planning more accurately. It separates uncertainty into task clarity, familiarity, and environmental risk, leveraging random network distillation to empirically estimate uncertainties. Tested on robot tasks, CURE outperforms existing methods by better predicting when plans may fail.", "questions": "How sensitive is CURE’s performance to the quality or size of the prior task dataset used for RND?\nWhat happens if task familiarity data is unavailable—can CURE still function effectively without RND?\nHave you tested generalization to new task types or domains not seen during training? Can you provide some examples of the tasks? \nCould you share more about the labeling effort required for training UAN (e.g., how many success/clarity labels were needed)?\nHow would you calibrate the predictions?\nWhy use different models (70B vs 8B) for different tasks? How would one pick given a new domain?", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- Fine-grained decomposition of uncertainty into task clarity, familiarity, and intrinsic uncertainty\n- Modular, plug-and-play design requiring no changes to the LLM planner\n- Compared with a range of baseline methods and show strong empirical performance across two task domains\n\nWeaknesses:\n- CURE requires more input data than baselines, including task clarity labels, execution outcomes, and prior tasks; especially prior tasks may not be easy to obtain given that LLMs are trained on internet-scale data\n- CURE involves supervised training of multiple components and tuning many hyperparameters; demands more computational resources and engineering effort than lightweight, prompt-based baselines\n- CURE does not generalize to new or unseen task domains without retraining", "was_revised": true, "final_justification": "The conceptual novelty of the proposed method is incremental. The method relies on multiple heuristic and data-dependent modules that make the system bulky and complex to train/tune and deploy. These overheads undermine the claimed plug-and-play nature of the approach, raising concerns about scalability and real-world practicality. The performance gain over KnowNo seems marginal in certain domains, while it removes the formal guarantees conformal prediction (KnowNo) provides. I won't strongly oppose the acceptance of this work if all other reviewers and the AC finds value in this work, hence would like to maintain weak reject.", "rebuttal": "Thank you for your thoughtful comments and valuable feedback. Below are our detailed responses. \n\n**W1. Requires more input data compared to baseline methods. acquiring prerequisite tasks might be challenging.**\n\nWe can obtain task datasets by simulating tasks in virtual environments, which is a common and effective way to gather data. In this paper, we employed the following two dataset generation methods:\n\n1. LLM-based generation: We first randomly select three items and then use a large language model to indirectly indicate the desired option. For example, if the selected items are water, oranges, and chips, the LLM might say, \"I am thirsty; give me something to quench my thirst.\" This constitutes a task dataset. \n\n2. Script-based direct generation: For instance, we generate water, oranges, and chips and set the task as placing the chips next to the fruits.\n\nFor more complex tasks, we can use virtual simulation environments to generate data and combine them with real-world datasets. While obtaining data for prerequisite tasks may present certain challenges, we believe these methods can effectively address the issue.\n\nMoreover, in a mature robotic system, pre-training typically requires numerous training examples. These pre-training examples can serve as prior tasks, thereby resolving the issue in practical applications.\n\n**W2. Training and Tuning of hyperparameters, resulting in higher computational and engineering costs.**\n\nThe primary computational cost comes from the UAN and RND components, both of which are relatively small multi-layer neural networks. Consequently, the inference cost is very low. Specifically, the UAN and RND networks are three-layer neural networks with 4096, 128, and 32 neurons, respectively. Each network inference consumes only 0.53 MFLOPs.\n\nThe feature vector computation can leverage caching at the end of the planning model’s execution, so it does not incur additional computational overhead. While training the networks indeed requires considerable resources, this process can be conducted in the cloud, eliminating the need for on-device computation. Moreover, the trained model can be reused multiple times. Overall, although the computational resource cost increases, it is justified given the improvements in safety.\n\nRegarding hyperparameter optimization, this paper uses the same hyperparameter settings for two distinct tasks, and the experimental results demonstrate that this configuration achieves good performance. Therefore, there is no need to spend too much effort on hyperparameter tuning.\n\n**W3. Cannot generalize without retraining.**\n\nThis is indeed an important consideration. However, the main goal of this paper is to enhance the reliability of embodied intelligence systems, reducing dangerous and unintended behaviors. Therefore, when the system encounters tasks outside the training set, we recommend refusing to execute them. This can be achieved by evaluating task familiarity. While this approach may sacrifice generalization capability, by refusing to perform unfamiliar tasks, we can significantly enhance the system's reliability.\n\n\n\n**Q1. How sensitive is the performance of the CURE model to the quality or scale of prior task datasets used for RND?**\n\nWe conducted experiments on dataset scale, and the results are as follows:\n\n|Training Set Size|100|300|1000|3000|10000|30000|100000|\n|---|---|---|---|---|---|---|---|\n|SR-HR-AUC|0.125|0.146|0.174|0.234|0.362|0.430|0.547|\n\nThe experimental results demonstrate that increasing the dataset scale significantly improves the performance of the CURE model. When the dataset reaches about 10,000 samples, the performance improvement becomes noticeable, and further scaling up to 100,000 samples still yields continuous gains.\n\nAdditionally, we investigated the impact of dataset quality on the model. The datasets in this paper were generated both via LLM (large language model) automation and manual definition. For specific generation methods, please refer to our response in W1. The model performed well on datasets constructed using these methods, indicating that CURE’s performance is relatively less dependent on data quality. In other words, the model can effectively utilize automatically generated datasets to achieve robust performance.\n\n**Q2. Can CURE still function effectively without RND, i.e., in the absence of task familiarity data?**\n\nYes. In Table 1, we present the experimental results of the CURE algorithm without using RND (i.e., CURE w/o sim). These results show that even without task familiarity data, CURE outperforms baseline methods and maintains a certain level of advantage.\n\n**Q3. Have you tested generalization to new task types or domains unseen during training?**\n\nWe tested on objects that were unseen during the training phase, and the results are as follows:\n\n1. Task: Give me a hamburger\n   Action: Pick up the hamburger and give it to the user.\n   Scene: An orange, an apple, and a hamburger.\n   Success Rate: 0.9565\n   Ambiguity: 0.3486\n   Task Familiarity: 0.0667\n   Confidence: 0.6898\n\n   In this example, although the hamburger is an unseen object, CURE can determine that the task instruction \"give me a hamburger\" is relatively clear, achieving a high success rate with a confidence level of 0.69.\n\n2. Task: Give me a hamburger\n   Action: Pick up the hamburger and place it in the bottom drawer.\n   Scene: An orange, an apple, and a hamburger.\n   Success Rate: 0.3540\n   Ambiguity: 0.3025\n   Task Familiarity: 0.0796\n   Confidence: 0.2102\n\n   In this example, the same task instruction \"give me a hamburger\" was executed incorrectly, with the model mistakenly placing the hamburger in the bottom drawer. The success rate is low (0.3540), and confidence drops to 0.21. These two examples demonstrate that the CURE model can accurately assess uncertainty even when handling unseen objects, showcasing a degree of generalization capability.\n\n3. Task: Heat the hamburger\n   Action: Pick up the hamburger and place it in the microwave.\n   Scene: An orange, an apple, and a hamburger.\n   Success Rate: 0.9311\n   Ambiguity: 0.7558\n   Task Familiarity: 0.2581\n   Confidence: 0.2508\n\n   The third example illustrates another scenario where the task instruction \"heat the hamburger\" is unseen in the training set, which is a new task type. Since this task is unfamiliar, the task familiarity is very low, leading to low confidence. This result reflects the model's ability to prevent execution of unknown actions, thereby avoiding potential risks.\n\n**Q4. Can you provide some example tasks? How much annotation effort (e.g., success/clarity labels) is required to train UAN?**\n\nIn this paper, we generated 100,000 training samples.\n\nCrucially, the generation of success/clarity labels for these 100,000 samples was achieved through automated methods (LLM simulation, scripted checks in simulation), requiring minimal manual intervention. This significantly reduces the practical labeling burden.\n\nAdditionally, we tested the results with varying training dataset sizes; please refer to the response to Q1 for detailed results.\n\nHere are some example tasks:\n\n#### Success Example 1\n\n- Scene: A bottled unsweetened tea, an orange, and a bag of jalapeño chips.\n- Task: Put jalapeño chips in the drawer.\n- Action: Pick up jalapeño chips and place them in the bottom drawer.\n- Success: 0.9624\n- Ambiguity: 0.9281\n- Familiarity: 0.0484\n- Confidence: 0.3781\n\n#### Success Example 2\n\n- Scene: A bag of kettle chips, an apple, and a Pepsi.\n- Task: Put the apple in the bottom drawer.\n- Action: Move the apple to the bottom drawer.\n- Success: 0.9763\n- Ambiguity: 0.1708\n- Familiarity: 0.0366\n- Confidence: 0.8396\n\nIn the first example, the task does not explicitly specify which drawer, resulting in higher task ambiguity. Despite relatively high success and familiarity rates, the execution confidence remains low. In the second example, the task explicitly specifies \"bottom drawer,\" significantly reducing task ambiguity. Although the expected success rate and task familiarity are similar to the first example, the clearer task instruction leads to significantly higher execution confidence.\n\n#### Failure Example\n\n- Scene: A RedBull, a bag of jalapeño chips, and an orange soda.\n- Task: Bring me something with a kick.\n- Action: Pick up RedBull.\n- Success: 0.8931\n- Ambiguity: 0.0566\n- Familiarity: 0.0840\n- Confidence: 0.7788\n\nThis is a typical failure case where the planned action is incorrect, yet the confidence is high. Here, the instruction is somewhat ambiguous; the user actually wants a bag of jalapeño chips, but the model incorrectly interprets the instruction and selects RedBull. This reflects the model’s limited understanding and failure to correctly interpret the instruction while mistakenly assuming the instruction is clear.\n\n**Q5. How to calibrate?**\n\nOur approach involved splitting the test set into two parts: one for calibration and the other for testing. Given a target success rate, we first determined a threshold on the calibration set. Tasks with confidence below the threshold were completed with human assistance, while tasks with confidence above the threshold were executed autonomously. By adjusting the threshold, we could make the success rate approach the target success rate, thereby achieving calibration.\n\nWe tested this method for a target success rate of 85%. The results showed that the success rate on the test set reached 84.33%, differing from the target by only 0.67%. This demonstrates that the CURE method achieves good calibration accuracy.\n\n**Q6. Why were different models used for different tasks?**\n\nSince the second tabletop placement task was relatively simple, we achieved satisfactory results with the 8B model. To save computational resources, we opted to use the 8B model.\n\n**Q7. How should the model be selected when encountering new domains?**\n\nIf computational resources allow, selecting a larger model generally leads to better performance."}, {"review_id": "0kqfI3YIDf", "reviewer": "Reviewer_FX41", "summary": "The paper presents CURE, a method for improving the reliability of robot planning using Large Language Models (LLMs) by accurately estimating uncertainty. CURE separates uncertainty into two main categories:\n- Epistemic Uncertainty: Split into task familiarity (measured using Random Network Distillation) and task clarity (evaluated via neural networks or direct LLM queries).\n- Intrinsic Uncertainty: Based on the expected success rate influenced by environmental factors.\n\nThe authors demonstrate the effectiveness of CURE in two robotic scenarios (kitchen manipulation and tabletop rearrangement), showing that it significantly outperforms baseline methods by providing uncertainty estimates better aligned with actual task outcomes.", "questions": "- Could you explicitly report the original metrics (such as overall success rates without uncertainty-based intervention) for IntroPlan and KnowNo?\n- Could you provide additional analysis explaining how your proposed metrics (SR-HR-AUC and Spearman correlation) interact differently with existing baselines and discuss specific scenarios or limitations where these metrics might misrepresent actual baseline performance?\n- Given that IntroPlan already implicitly addresses epistemic uncertainty via introspective knowledge-base retrieval, could you clarify the practical advantages and potential trade-offs (e.g., scalability, complexity) of explicitly modeling task similarity, ambiguity, and expected success rate using separate neural networks? Additionally, can you provide qualitative examples illustrating success and failure cases of your method (CURE)?", "limitations": "Yes. The authors discussed limitations.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- The author proposes an interesting method that differentiates between epistemic and intrinsic uncertainties.\n- Proposes SR-HR-AUC and Spearman correlation to quantify uncertainty estimation performance\n- Demonstrates improvement over existing baselines in two diverse scenarios: kitchen manipulation and tabletop rearrangement tasks.\n\nWeakness:\n1. While the paper proposes novel metrics (SR-HR-AUC and Spearman correlation) to assess uncertainty estimation, it's crucial to also present and analyze previously established metrics (like overall success rate) clearly. Omitting these original metrics can lead to incomplete evaluations or potentially biased comparisons.\nSpecifically, regarding the IntroPlan baseline, the authors briefly attribute its low performance on the new metrics to a high initial success rate, yet they don't explicitly report or quantify this original success rate. Without this information, it is challenging to fairly assess the baseline performance.\nTo address this, the authors should explicitly:\n- Report original metrics in IntroPlan and KnowNo (e.g. success rate)\n- Provide more analysis why these new metrics negatively influence some baseline performance.\n- Discuss limitations or scenarios where the proposed metrics might misrepresent certain baselines.\n\nIncluding these analyses would strengthen the evaluation.\n\n2. It’s worth including some qualitative examples to better understand how the proposed method is effective and the failure cases of the proposed method.\n\n3. I highly doubt the necessity of training two additional networks to explicitly predict task similarity, task ambiguity, and expected success rate. Introspective planning already effectively leverages prior knowledge from the knowledge base, implicitly reducing epistemic uncertainty by retrieving relevant historical interactions that inherently capture task similarity and clarity. Although explicitly modeling these factors through additional neural networks appears interesting, it might be unnecessary and potentially less scalable compared to IntroPlan. Therefore, I suggest the authors add a more thorough discussion about these trade-offs, provide a fairer empirical comparison with prior works (especially IntroPlan) and clearly articulate which specific aspects or components of CURE offer practical advantages.", "was_revised": true, "final_justification": "The author addressed most of the concerns during the rebuttal. I think the paper will be strengthened if the author incorporates all the additional results and discussions into the paper.", "rebuttal": "Thank you for your thoughtful comments and valuable feedback. Below are our detailed responses. \n\n**W1. Provide further analysis explaining why these new metrics negatively impact some baseline performances.**\n\n\nRegarding the poor performance of IntroPlan on the new metrics, our hypothesis is that it possesses more comprehensive information, making the model overly confident in its chosen answers. Below are the confidence distribution statistics for IntroPlan and KnowNo in the test questions:\n\n**KnowNo:**\n- Average Confidence: 0.320416\n- Minimum Confidence: 0.214667\n- Maximum Confidence: 0.498671\n- Standard Deviation: 0.054888\n\n**IntroPlan:**\n- Average Confidence: 0.905210\n- Minimum Confidence: 0.300099\n- Maximum Confidence: 1.000000\n- Standard Deviation: 0.198491\n\nIn IntroPlan, there are 242 items with confidence values exceeding 0.99, which leads to low differentiation in confidence levels. Our new metrics (SR-HR-AUC and Spearman correlation) mainly focus on the differentiation of confidence between successful and failed tasks, thus resulting in poor performance for IntroPlan on these two metrics.\n\n\n\n**Q1. Can you explicitly report the original metrics for IntroPlan and KnowNo?**\n\nFigure 6 in the original text shows the overall success rate when there is no uncertainty-based intervention. When the Help Rate is 0, it represents the overall success rate. The specific success rates are as follows:\n\n\n|  Method        | KnowNo | CURE  | IntroPlan |\n|--------------------|--------|-------|-----------|\n| Initial Success Rate ↑ | 40.67% | 40.67% | 63.00%    |\n\nSince CURE is a subsequent step based on the planning results of KnowNo, the original metrics should be consistent with KnowNo. Therefore, no additional explanation is provided in the paper.\n\n**Q2. Can you provide additional analysis explaining how your proposed metrics interact differently with existing baselines and discuss specific scenarios or limitations where these metrics might distort actual baseline performance?**\n\nThe metrics we propose differ significantly from the objectives of existing baseline tests. Existing baselines primarily focus on the overall success rate of tasks, aiming to enhance the performance of planning models. In contrast, our proposed metrics (SR-HR-AUC and Spearman correlation) emphasize the accuracy of uncertainty to prevent robots from exhibiting unexpected behavior. This metric is similar to the Overstep Rate in IntroPlan but still differs:\n\nOverstep Rate is defined as the proportion of overconfident or incorrect options generated by the planner when certain. It is calculated as: Count (robot is certain but wrong)/Count (robot is certain).\n\nWe conducted additional experiments to evaluate the Overstep Rate, Overask Rate, and Help Rate of the IntroPlan method, CURE method, and IntroPlan+CURE method when the target success rate is 90%. The experimental results are shown below:\n\n| Metric            | CURE   | IntroPlan + CURE | IntroPlan |\n|-------------------|--------|------------------|-----------|\n| **Overstep Rate** ⬇  | 34.88% | **21.58%**      | 31.82%    |\n| **Help Rate** ⬇     | 71.33% | 53.67%          | **19.33%** |\n| **Overask Rate** ⬇   | **29.44%** | 53.42%       | 51.72%    |\n\nThe results show that the IntroPlan + CURE method achieved the absolute optimal effect in terms of Overstep Rate, significantly reducing unsafe incidents. In terms of Help Rate, IntroPlan demonstrated the lowest rate, indicating higher confidence and reduced need for human intervention. However, considering the small difference between IntroPlan + CURE and IntroPlan in the Overask Rate metric, it suggests that the help behavior of IntroPlan + CURE was not ineffective, significantly improving safety performance. CURE performed best in the Overask Rate, partly due to the lower initial success rate of its base method, KnowNo, making the effectiveness of the help more significant.\n\n**Q3. Given that IntroPlan implicitly addresses epistemic uncertainty through introspective knowledge base retrieval, can you clarify the actual advantages and potential trade-offs (e.g., scalability, complexity) of explicitly modeling task similarity, ambiguity, and expected success rate using separate neural networks? Additionally, can you provide qualitative examples to illustrate the success and failure cases of your method (CURE)?**\n\nCompared to IntroPlan, the proposed method has significant advantages in scalability. A typical example is that IntroPlan can be integrated with CURE to further enhance system performance. As demonstrated in the experiments concerning the previous question, when IntroPlan is used in conjunction with CURE, both the sr_hr_area and Overstep Rate (with a target success rate of 90%) improve compared to using IntroPlan alone. This indicates that there is still room for improvement in the accuracy of uncertainty estimation in IntroPlan, and by introducing CURE, this accuracy can be further optimized.\n\nRegarding complexity, the main disadvantage of the method proposed in this paper is the need for a large training set for model training. However, considering that a mature robotic system typically requires numerous training cases for pre-training, this limitation has relatively little impact on practical applications, making this disadvantage acceptable in practice.\n\nExamples of CURE are as follows:\n\n#### Success Example 1\n- **Scene**: a bottled unsweetened tea, an orange, and a bag of jalapeno chips\n- **Task**: Put jalapeno chips in the drawer.\n- **Action**: pick-up jalapeno chips to bottom drawer\n- **Success**: 0.9624221324920654\n- **Ambiguity**: 0.9281229376792908\n- **Familiarity**: 0.04835970117710531\n- **Confidence**: 0.3781147508416325\n\n#### Success Example 2\n- **Scene**: a bag of kettle chips, an apple, and a Pepsi\n- **Task**: Put that apple in the bottom drawer.\n- **Action**: move apple to bottom drawer\n- **Success**: 0.9762726426124573\n- **Ambiguity**: 0.17079491913318634\n- **Familiarity**: 0.03660911461338401\n- **Confidence**: 0.8396180854178965\n\nIn the above two examples, the first example has a high task ambiguity due to the lack of specification on which drawer. Despite high expected success and familiarity, the execution confidence remains low. The second example clearly specifies \"bottom drawer,\" significantly reducing task ambiguity. Although the expected success and task familiarity are similar to the first example, the execution confidence is significantly improved due to the clearer task instructions.\n\n#### Failure Example\n\n- **Action**: pick up RedBull\n- **Scene**: a RedBull, a bag of jalapeno chips, and an orange soda\n- **Task**: Bring me something with a kick.\n- **Success**: 0.8931169509887695\n- **Ambiguity**: 0.056598808616399765\n- **Familiarity**: 0.08403261657804251\n- **Confidence**: 0.7787547493353486\n\nThis is a typical failure example where the planned action is incorrect, but the confidence is high. The instruction is quite ambiguous here; the user actually wanted a bag of jalapeno chips, but the model mistakenly interpreted it as requiring RedBull. This reflects a lack of understanding from the model, failing to correctly interpret the instruction while erroneously perceiving it as clear."}, {"review_id": "4R22cubevB", "reviewer": "Reviewer_mEEn", "summary": "The authors carry out an important and interesting study to better detect LLM uncertainty in robot planning. The authors divide the whole uncertainty into task familiarity and task clarity and then propose methods to estimate the uncertainty of both factors. The task familiarity is estimated by training one distilled NN so that the unexpected instructions naturally cause larger different between distilled NN and the original policy model. Task clarity is assessed by prompting LLM to answer with uni- or multi-goals.\n\nThe study and proposed methods are interesting and new. However, there are some questions remained on the soundness of the method, the framework setting, and the testing tasks. Please see the following discussion.", "questions": "1) I wonder how the authors construct the training dataset for UAN? How to choose the tasks and plans, also how to judge whether the subtasks are successfully executed?\n\n2) The generalizability may remain a problem. Even for the same set of tasks, expressing with different formats, such as 'picking up sprite' vs. 'I would like to serve the sprite to the user because...'. Whether these different expressions will make the system deviates from the training distribution?\n\n3) As for the two testing environments, it will be better to show specific examples so that the readers can better understand the testing capability of LLMs. For example, is the LLM required to output the full action sequence with pre-defined formats and action options?\n\n4) What will be the reason the robot executes the tasks and fails? Is this due to the wrong action plan or the wrong motion planning? If the motion planning is not grounded to the real world, the testing in the robot scenario is the same as the testing in the digital world like 2D games. Hence, what is the difference of applying this framework to robot with digital games?", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: The problem setting is important, to better probe the LLM uncertainty during robot planning to better decrease the risks. The proposed methods to divide the uncertainty and estimate via distilled NN are new and interesting. It is the first time I find studies estimate LLM uncertainty like this work.\n\nWeakness:\n1) Why the uncertainty can be divided into task familiarity and task clarity. I guess many uncertainty in task familiarity also relates to task clarity. The two testing tasks in the study is relatively simple. However, there are many hierarchical instructions in robot applications that more complex task and motion planning needed. In these conditions, task familiarity and clarity relates to each other. For example, instead of 'visit building A, then B, then C', 'visit all buildings with shortest path'. It seems the less task clarity will lead to less task familiarity.\n\n2) The way to estimate task familiar and clarity. These methods are new and interesting. However, can there be any clarification to support these designs are reasonable and sound? Why the task should be regarded as unfamiliar if the gaps between original and distilled NNs are larger? Maybe it also results from the ill-trained distilled NN?\n\n3) The testing environments and instructions are simple. It seems no motion planning part is related and the LLM is required only for simple sub-task or even uni-task choices. The generalizability is limited since the trained NN should have pre-designed dataset related to testing tasks. Hence, the online adaptation will not be available.", "was_revised": true, "final_justification": "I think overall the paper is good in novelty, though the experiments and ablation studies are not enough. I recommend weak accept in this case, while I am also open to other reviewers' decisions and comments.", "rebuttal": "Thank you for your thoughtful comments and valuable feedback. Below are our detailed responses. \n\n**W1. Why can uncertainty be divided into task familiarity and task clarity?**\n\nThank you for your valuable feedback. The potential correlation between task familiarity and task clarity in complex scenarios, as pointed out, is very reasonable. However, the definition of \"task clarity\" in this paper differs from the focus you described. The concept of \"task clarity\" you mentioned is closer to the **level of detail** of the task.\n\n1. **Core Definition of Task Clarity:** In this paper, task clarity primarily assesses whether the instructions themselves provide sufficient information for the robot to perform the correct operation to fulfill the user's need. Taking the two examples you provided:\n    * \"Visit building A, then B, then C.\"\n    * \"Visit all buildings with the shortest paths.\"\n   Both instructions are sufficiently clear, and the robot can execute the corresponding tasks based on the instructions. However, consider another scenario: the user's need is for the robot to \"visit the tallest building,\" but due to the ambiguity of human language, the operator's actual instruction might be \"visit that building\" without specifying which one (missing the \"tallest\" key information). In this case, the robot will struggle to complete the task correctly. The core of task clarity as defined in this paper lies in addressing the consistency between the instruction information and the user's need. Therefore, task clarity does not refer to the level of detail of the instructions, but whether the instruction unambiguously conveys the essential elements needed to complete the task as intended.\n\n2. **Necessity of Distinguishing Clarity and Familiarity:** Based on the above definition, if the task instructions themselves lack clarity, the robot cannot correctly complete the task, even if it is very familiar with actions like \"visiting buildings.\" Therefore, this paper considers it necessary and meaningful to distinguish task clarity (the clarity of instruction conveying the need) and task familiarity (the proficiency in executing specific types of tasks) as two distinct dimensions.\n\n**W2. Methods for evaluating task familiarity and clarity.  However, is there any explanation to support the rationality and reliability of these designs?**\n\nYour point is very valuable. Regarding the rationality of the task familiarity evaluation method, its design inspiration comes from Random Network Distillation[1]. This method has been widely used in the field of reinforcement learning to assess the novelty of states. The task familiarity metric is mainly used for relative comparison. Indeed, if the distilled neural network is poorly trained, it might lead to an overall high level of gaps corresponding to all tasks, but the relative size differences between different tasks still exist. Therefore, this metric can still effectively reflect the relative familiarity of tasks.\n\nTask clarity can be obtained through supervised learning on the training set or inferred using large language models.\n\n**W3. This method has limited generalization capability because the trained neural network is supposed to contain preset datasets related to the test tasks.**\n\nYour point is very meaningful. The main goal of the paper is to enhance the reliability of embodied intelligent systems and reduce potential dangerous and undesirable behaviors. Therefore, when the system receives tasks outside the training set, opting to refuse execution is a well-considered strategy (our method uses task familiarity evaluation to perform refusal). Although this strategy may limit generalization ability in some situations, we believe this trade-off is worthwhile as it significantly improves the system's reliability and safety.\n\n\n\n**Q1. How the authors constructed the training dataset for UAN. How were tasks and plans selected, and how was the successful execution of sub-tasks judged?**\n\nThe construction of the UAN training dataset is mainly achieved through the following two methods:\n\n1. **Based on LLM Generation:**\n    * Randomly select three objects.\n    * Then, guide the LLM to specify the desired object indirectly. For example, given objects `water`, `orange`, `chips`, the LLM might generate a task description: \"I'm thirsty, give me something to quench my thirst.\"\n    * This constitutes a task sample:\n        * `scene`: Mainly `water`, `orange`, `chips`\n        * `task`: \"I'm thirsty, give me something to quench my thirst\"\n    * **Success Judgment:** If the agent's action is to give `water` to the user, it is considered successful; otherwise, it is considered a failure.\n\n2. **Based on Script Generation:**\n    * Directly generate scenes and tasks through scripts. For example, generate a scene containing `water`, `orange`, `chips`, with the task description: \"Place the chips next to the fruit.\"\n    * **Success Judgment:** If the agent's action is to place `chips` next to `orange`, it is considered successful; otherwise, it is considered a failure.\n\n> **Note:** The actual generation process is more complex than the above examples. Please refer to the `dataset_generate.py` file in the code for specific implementation details.\n\n**Regarding the generation of task planning (Plan):**\nThe planning generation method adopted in the paper follows the method of the KnowNo[2], which involves querying the LLM to generate task planning. \n\n**Q2. Expressed in different formats, such as \"pick up Sprite\" vs. \"I want to provide Sprite for the user because...\". Could these different expressions cause the system to deviate from the training distribution?**\n\nAfter the LLM completes the planning, we implemented an additional grounding step to align planning actions with robot executable actions. Therefore, the two different expression formats you mentioned will ultimately be unified into the standard form of \"pick up Sprite for the user.\" This step not only ensures the reliability of subsequent execution but also effectively guarantees that the system does not deviate from the training distribution.\n\nFor more complex or novel actions that cannot be grounded, considering the safety requirements of the embodied intelligent system, such tasks fall outside the training distribution and will be rejected for execution by the system based on task familiarity assessment. While this approach sacrifices some generalization capability, it significantly enhances the operational safety and reliability of the robot system.\n\n**Q3. Regarding the two test environments, show specific examples to help readers better understand the testing capabilities of the LLM.**\n\nIn this experiment, the action format is predefined as either placing an item in a location or giving the item to the user.\n\nThank you for your suggestion, here are specific examples.\n\n### Complete Procedure for Kitchen Operation Experiment\n\nDue to length limitations, please refer to the response to item 5 under \"Limitations\" in \"Rebuttal by Authors For Reviewer hD99\" for the specific details of this example.\n\nSome brief details are as follows:\n\nScene: a bottled unsweetened tea, an orange, and a bag of jalapeno chips \nTask: Put jalapeno chips in the drawer.\n\nBased on this, we generate action with KnowNo[2]. \n\nAction: pick-up jalapeno chips to bottom drawer\n\nNext, we combine the aforementioned content into a prompt and input this prompt into the Llama3-8B model and extract the feature vector $T$.\n\nSubsequently, the feature vector is input into the UAN and RND network to obtain the final expected success rate $p$, ambiguity $A_{\\text{amb}}$, and familiarity $A_{\\text{sim}}$:\n\n- $p$: 0.962\n- $A_{\\text{amb}}$: 0.928\n- $A_{\\text{sim}}$: 0.048\n\nFinally, the confidence value in the example is 0.378.\n\n\n### Desktop Rearrangement Experiment Process\n\nThis process is similar to the kitchen operation experiment, with some specific details as follows:\n\nScene: blue block, yellow bowl, yellow block, green bowl, green block, blue bowl\nTask: put the yellow block in the yellow bowl.\nAction: put the green block to the back of the blue bowl.\n\nCombined prompt is:\n\n```\nYou are a human and there is a robot. The robot is in front of a table.\nOn the table there are these objects: blue block, yellow bowl, yellow block, green bowl, green block, blue bowl.\nThe robot is asked to put the yellow block in the yellow bowl.\nThen the robot put the green block to the back of the blue bowl.\n```\n\n- $p$: 0.181\n- $A_{\\text{amb}}$: 0.185\n- $A_{\\text{sim}}$: 0.051\n\nConfidence value is 0.109.\n\n**Q4. What are the reasons for the robot failing to execute tasks? What is the difference in applying this framework to robots versus digital games?**\n\nHere is a failed example:\n#### Failure Example\nScene: a RedBull, a bag of jalapeno chips, and an orange soda\nTask: Bring me something with a kick.\nAction: pick up RedBull\nSuccess: 0.893\nAmbiguity: 0.056\nFamiliarity: 0.084\nConfidence: 0.778\n\nThis is a typical failure case: the generated **action** is incorrect, yet the **confidence** is high. Specifically, the task instruction \"Bring me something with a kick\" is quite ambiguous. The user actually needs \"a bag of jalapeno chips,\" but the model erroneously chose \"RedBull,\" resulting in an excessively high confidence. This is mainly due to the model's inadequate understanding of the instruction and overestimating the instruction clarity.\n\nThe uncertainty estimation method proposed in this paper is mainly aimed at the action plan. Therefore, the proposed method is applicable to both robot scenes and digital worlds. And there is no difference because the experiment does not involve motion planning.\n\n**References**\n\n[1] Burda, Y., Edwards, H., Storkey, A., & Klimov, O. (2018). Exploration by random network distillation. arXiv preprint arXiv:1810.12894.\n\n[2] Ren, Allen Z., et al. \"Robots that ask for help: Uncertainty alignment for large language model planners.\" arXiv preprint arXiv:2307.01928 (2023)."}, {"review_id": "rSjhi1tMHN", "reviewer": "Reviewer_hD99", "summary": "The paper presents a method to disentangle uncertainties in planning tasks and estimate them separately. It disentangles between epistemic uncertainty---broken into task clarity and task familiarity---and intrinsic uncertainty, defined as the likelihood of successfully producing a plan. The method enables a more accurate and comprehensive uncertainty assessment for LLM-driven planning. Empirically, the paper compares the proposed uncertainty estimation algorithm with SOTA methods used in robot planning and LLMs. Experiments on mobile manipulation and tabletop rearrangement tasks demonstrate accurate and comprehensive uncertainty estimates, with the strongest correlation to actual execution outcomes.", "questions": "Q1. In the problem statement, the observation *O* is an \"instruction pertaining to the current environment.\" In my understanding, *O* is a description of the current environment. Is it correct? Is there a realistic example of *O*?\n\nQ2. A follow-up to Q1: Is this method applicable to visual observation? There is an image example in Figure 1. What if you use the image as input to a foundation model with vision capability instead of inputting the text-based environment context?\n\nQ3. How is the success rate measured? If I understand correctly, an \"expected success rate\" is an estimation of an actual success rate, i.e., task completion rate. Then, how is the actual success rate measured? Do you run the experiments on robots or simulators multiple times and count the number of successful cases?\n\nQ4. How is Equation 4 formulated? Is there a discussion on why the equation in this form is most effective? Are there any ablation studies that show that each component of the equation is effective?", "limitations": "L1. Some important terminologies are not clearly defined. For example, there is no formal definition of *epistemic* and *intrinsic* uncertainty. As these terminologies are key components in the paper, a reference to other papers that define the terms may not be sufficient. For clarity, maybe include a few definition blocks in the preliminary section.\n\nL2. While the method estimates uncertainty, it lacks a discussion of how to resolve the uncertainty. For example, using the estimated uncertainty as a reward signal for fine-tuning the LLM or refining the input prompts.\n\nL3. While the empirical results demonstrate a more accurate uncertainty estimation compared with existing baselines, the reliance on neural networks (MLP) leads to a higher computation overhead compared with simple confidence calibration (e.g., KnowNo). This looks like a complexity-accuracy trade-off rather than \"outperform\" existing baselines.\n\nL4. Some baselines, such as TopK and Vanilla, are too trivial to demonstrate the effectiveness of the proposed method. Instead of showing that the method outperforms the baselines in uncertainty estimation, it is better to demonstrate that the proposed method can improve the task completion rate in some ways.\n\nL5. The appendix does not provide sufficient experimental details. For example, could you please provide real prompts rather than placeholders such as *[What will you do Here]*?", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\nThe paper presents an interesting approach that separately estimates epistemic and intrinsic uncertainty, enabling better uncertainty interpretation in robotic tasks.\n\nEmpirical studies demonstrate that the proposed algorithm significantly enhances the accuracy of uncertain estimation, outperforming existing methods in embodied planning and LLM uncertain estimation.\n\n**Weaknesses**\n\nPlease see *Limitations* below.", "was_revised": true, "final_justification": "The rebuttal has addressed all my concerns by adding new results and examples. I will increase the score, assuming that they will integrate the results into the paper during revision.", "rebuttal": "Thank you for your thoughtful comments and valuable feedback. Below are our detailed responses. \n\n**Q1. O is a description of the current environment. Is that correct? Are there any real examples of O?**\n\nYes, you are correct. An example of $O$ is as follows:\n\n> You are a robot operating in an office kitchen. You are in front of a counter with two closed drawers, a top one and a bottom one. There is also a landfill bin, a recycling bin, and a compost bin. On the counter, there is an orange soda, a Pepsi, and an apple.\n\n**Q2. Does this method apply to visual observations? What if the image is used as input for a foundational model with visual capabilities?**\n\nIn the specific implementation presented in this paper, which uses LLaMA, images cannot be directly inputted as LLaMA only supports text input. Therefore, it is necessary to first convert the image into a text description using a VLM before feeding it to subsequent models for processing.\n\nHowever, if a multimodal model supporting images (such as LLaVA) is adopted and the text encoder is replaced with the corresponding multimodal encoder, images can be directly inputted and encoded into feature vectors, keeping the subsequent process unchanged.\n\n**Q3. How is the success rate measured? So, how is the actual success rate measured? Did you conduct multiple experiments?**\n\nThe success rate measurement method is as follows:\n\nIn the constructed training set and test set, each sample (case) has only one clear binary label: successful cases are marked as 1, and failed cases are marked as 0.\n\nSuccess is defined as whether the robot's action meets the user's requirements. For example, if the user needs a caffeinated beverage and the robot provides Red Bull, it is considered a success (label 1); otherwise, it is a failure (label 0).\n\nNote that here each sample has only a single binary label, without involving multiple repeated experiments to calculate probabilities.\n\nYou understand correctly; the \"expected success rate\" is indeed an estimate of the actual success rate.\n\nThe expected success rate is provided by the model. After training on the training set, the model outputs a value between 0 and 1 as the \"expected success rate\" for that sample.\n\nNeither the actual success rate nor the expected success rate is statistically calculated through multiple experiments.\n\n\n\n**Q4. How is Equation 4 formulated? Has there been a discussion on why this form of equation is most effective?**\n\nEquation 4 is designed to comprehensively reflect uncertainty in the planning process, combining three main factors: task clarity, expected task success rate, and task similarity.\n\n1. **Task Clarity**:\nTask clarity is evaluated using two methods: a query-based LLM method and a multi-layer neural network inference method. Both results are represented as $A_{\\text{amb}}$, reflecting the ambiguity or difficulty of understanding the task.\n\n2. **Expected Task Success Rate**:\nPredicted using a multi-layer neural network, yielding success rate $ p $, representing the likelihood of completing the task given the conditions.\n\n3. **Task Similarity**:\nEstimated using RND to obtain task similarity, $A_{\\text{sim}}$, indicating the similarity of the current task to known tasks.\n\nThe Equation reflects uncertainty's comprehensive performance under different task conditions through a weighted combination of these three factors. \n\nWe formulate Equation 4 as a heuristic model to simulate the generation of uncertainty. It starts from maximum uncertainty (1), subtracting the influence of success rate and task clarity correction to increase certainty, while compensating for extra certainty brought by task familiarity through negative weight parameters. The equation simulates human thinking processes when facing uncertainty, considering the influence of success rate, task clarity, and familiarity comprehensively.\n\nSome ablation study results are integrated into Section 5.1, Table 1. The complete ablation study results are as follows:\n\n|Ablation Condition| Task Clarity | Expected Task Success Rate | Task Similarity | SR-HR-AUC⬆ |\n|-|-|-|-|-|\n|Success Only| | √|| 0.211|\n|Similarity Only||| √ | 0.135 |\n|Clarity only| √ | || 0.173    |\n|Remove Task Similarity| √ | √| | 0.483    |\n|Full Model (All Enabled)| √ | √| √ | 0.534    |\n\n\n\n**L1 & L2. Some important terms are not clearly defined.It lacks discussion on how to resolve uncertainty.**\n\nThank you for your suggestion. We will add this part of the definitions and discussions to explore possible directions for reducing uncertainty to the paper. \n\n**L3. The reliance on neural networks (MLP) leads to higher computational.**\n\nThe computational resources are mainly consumed in the UAN and RND parts, which are both relatively small multi-layer neural networks, resulting in very low computational consumption during actual inference. Specifically, the UAN network and RND network both have a three-layer structure, with neuron numbers of 4096, 128, and 32 respectively. The computational consumption of a single inference is only 0.53 MFLOPs, requiring relatively few computing resources.\n\nAdditionally, the computation of feature vectors can utilize the cache after planning by the planning model, avoiding extra computational resource consumption. Although the network training process does require significant resource investment, this part of the computation can be completed in the cloud, not requiring execution on the device, and can be reused after training once.\n\nOverall, although there is an increase in computational resource cost, it is almost negligible. On the other hand, the significant improvement in safety makes this cost increase trivial.\n\n**L4. Some baselines are too trivial to demonstrate the effectiveness of the proposed method.**\n\nThank you for your suggestion. Since the main focus of this paper is on reducing unexpected actions, we have added some experiments comparing some new metrics, which are defined in IntroPlan[1]. Meanwhile, we tested the IntroPlan method combined with the CURE method.\n\nThe experimental results of Overstep Rate, Overask Rate, Help Rate (target success rate 90%) for the IntroPlan method, CURE method, and IntroPlan+CURE method are as follows:\n\n| Metric              | CURE   | Introplan + CURE | Introplan |\n|---------------------|-----------|------------------|-----------|\n| **Overstep Rate** ⬇ | 34.88%    | **21.58%**           | 31.82%    |\n| **Help Rate** ⬇     | 71.33%    | 53.67%           | **19.33%**    |\n| **Overask Rate** ⬇  | **29.44%**    | 53.42%           | 51.72%    |\n\nIt can be seen that:\n- In terms of Overstep Rate, the Introplan + CURE method achieved absolutely optimal results, greatly reducing the occurrence of unsafe events.\n- In terms of Help Rate, Introplan is the lowest, indicating it is most confident and requires the least human intervention. However, considering the small gap in Overask Rate between Introplan + CURE and Introplan, it suggests that the help requests of Introplan + CURE are not ineffective, significantly improving safety performance.\n- In terms of Overask Rate, the CURE method is the best, attributed to the initial low success rate of the KnowNo basic method, making the effective help range larger.\n\n**L5. The appendix does not provide enough experimental details.**\n\nYou are correct, here is a real prompt:\n\n### Complete Procedure for Kitchen Operation Experiment\n\nFirst, we grasp the initial information including the scene and task, for example:\n\nScene: a bottled unsweetened tea, an orange, and a bag of jalapeno chips \nTask: Put jalapeno chips in the drawer.\n\nNext, we supplement the complete scene information to form a complete scene description:\n\n```\nWe: You are a robot operating in an office kitchen. You are in front of a counter with two closed drawers, a top one and a bottom one. There is also a landfill bin, a recycling bin, and a compost bin.\nWe: On the counter, there is an orange soda, a Pepsi, and an apple.\n```\n\nBased on this, we generate action according to the complete scene and task. This generation process is consistent with KnowNo[2]. \nIt is emphasized that this generation method is independent of the method proposed in this paper and can adopt any method to generate operation instructions. The generated operation instruction is (if the instruction is not in this format, it will be reformatted by LLM):\n\n```\naction: pick-up jalapeno chips to bottom drawer\n```\n\nNext, we combine the aforementioned content into a prompt with specific content:\n\n```\nYou are a human and there is a robot operating in an office kitchen. The robot is in front of a counter with two closed drawers, a top one and a bottom one. There is also a landfill bin, a recycling bin, and a compost bin.\nOn the counter, there is {scene}.\nYou say: \"{task}\".\nThen the robot {action}.\n```\n\nWe input this prompt into the Llama3-8B model and extract the last layer's hidden state (last_hidden_state) as the feature vector $T$.\n\nSubsequently, the feature vector is input into the UAN network and RND network to obtain the final expected success rate $p$, ambiguity $A_{\\text{amb}}$, and familiarity $A_{\\text{sim}}$:\n\n- $p$: 0.962\n- $A_{\\text{amb}}$: 0.928\n- $A_{\\text{sim}}$: 0.048\n\nFinally, the uncertainty (Uncertainty) is calculated using Eq.4; Confidence is calculated as: 1 - U. The confidence value in the example is 0.378.\n\n**References**\n\n[1] Liang, Kaiqu, Zixu Zhang, and Jaime F. Fisac. \"Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity.\" Advances in Neural Information Processing Systems 37 (2024): 71998-72031.\n\n[2] Ren, Allen Z., et al. \"Robots that ask for help: Uncertainty alignment for large language model planners.\" arXiv preprint arXiv:2307.01928 (2023)."}]

Author rebuttal:

Meta-review: This paper proposes a finer-grained uncertainty quantification analysis in LLM planning inspired by RL: epistemic uncertainty and intrinsic uncertainty. The former concerns uncertainty at the task level, whereas the latter is about uncertainty around executing a fixed plan. The paper proposes a system called CURE for explicitly modeling these two types of uncertainty, which they evaluate in kitchen manipulation and tabletop settings.

This paper is quite borderline, but the authors did a thorough job during the discussion period to address a majority of the reviewer concerns around comparison to baselines, ablation studies, generalization capabilities, and calibration. I believe that after incorporating the proposed changes in the author’s general response, the paper warrants being accepted. The problem formulation is important, the paper proposes new evaluation metrics beyond just success rate which is important as well (and they discuss this in detail in both the paper and rebuttal), and while I share the concerns of Reviewer 9hBH that the proposed solution is heuristic in nature and has quite a few components, empirically the system performs well and is a solid basis for further research to build on. Also, I believe conformal techniques can also be applied to CURE, and I encourage the authors to investigate this for the camera-ready.
Decision: Accept (poster)