Self-Refining Vision Language Model for Robotic Failure Detection and Reasoning
Authors: Carl Qi
Xiaojie Wang
Silong Yong
Stephen Sheng
Huitan Mao
sriram srinivasan
Manikantan Nambi
Amy Zhang
Yesh Dattatreya
Venue: ICLR 2026 Poster (2026)
Keywords: Failure Reasoning
Robotics
Foundation Models
Primary area: applications to robotics, autonomy, planning
TL;DR: We leverage a self-refining VLM to do open-vocabulary failure reasoning on robotic manipulation tasks.

Abstract: Reasoning about failures is crucial for building reliable and trustworthy robotic systems. Prior approaches either treat failure reasoning as a closed-set classification problem or assume access to ample human annotations. Failures in the real world are typically subtle, combinatorial, and difficult to enumerate, whereas rich reasoning labels are expensive to acquire. We address this problem by introducing ARMOR: Adaptive Round-based Multi-task mOdel for Robotic failure detection and reasoning. We formulate detection and reasoning as a multi-task self-refinement process, where the model iteratively predicts detection outcomes and natural language reasoning conditioned on past outputs. During training, ARMOR learns from heterogeneous supervision - large-scale sparse binary labels and small-scale rich reasoning annotations - optimized via a combination of offline and online imitation learning. At inference time, ARMOR generates multiple refinement trajectories and selects the most confident prediction via a self-certainty metric. 
Experiments across diverse environments show that ARMOR achieves state-of-the-art performance by improving over the previous approaches by up to 30\% on failure detection rate and up to 100\% in reasoning measured through LLM fuzzy match score, demonstrating robustness to heterogeneous supervision and open-ended reasoning beyond predefined failure modes. We provide dditional visualizations on our website: https://sites.google.com/utexas.edu/armor.

Published as a conference paper at ICLR 2026
SELF-REFINING VISION LANGUAGE MODEL FOR ROBOTIC FAILURE DETECTION AND REASONING
Carl Qi', Xiaojie Wang", Silong Yong', Stephen Sheng', Huitan M Sriram Srinivasan" Manikantan Nambi", Amy Zhang', Yesh Dattatreya² UT Austin Amazon Robotics Carnegie Mellon University carlqeutexas.cm edu
ABSTRACT
Reasoning about failures is crucial for building reliable and trustworthy robotic systems. Prior approaches either treat failure reasoning a closed-set classifi- cation problem or assume access to ample human annotations. Failures the real world are typically subtle, combinatorial, and difficult to enumerate, whereas rich reasoning labels are expensive to acquire. We address this problem by in- troducing ARMOR: Adaptive Round-based Multi-task mOdel for Robotic failure detection and reasoning. We formulate detection and reasoning as a multi-task self-refinement process, where the model iteratively predicts detection outcomes and natural language reasoning conditioned on past outputs. During training, AR- MOR learns from heterogeneous supervision large-scale sparse binary labels and small-scale rich reasoning annotations optimized via combination of of- fline and online imitation learning. At inference time, ARMOR generates mul- tiple refinement trajectories and selects the most confident prediction via self- certainty metric. Experiments across diverse environments show that ARMOR achieves state-of-the-art performance by improving over the previous approaches by up to 30% on failure detection rate and up to 100% in reasoning measured through LLM fuzzy match score, demonstrating robustness heterogeneous supervision and open-ended reasoning beyond predefined failure modes. We provide dditional visualizations on our website: htttps://sites google. com/ ut exas. edu/ armor
INTRODUCTION
Autonomous robotics has the potential improve process efficiency and people's well-being, through several applications ranging from warehouse operations (Mitash et al. 2023 Narang al. 2022 to household and assistive robots (Miller) 2006 Qi et al., 2022 Lin et al.) 2022 Black et al, To achieve true success in these applications,
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intelligent robot should not only follow instructions but also reason about task execution and, upon failure, provide explanations that enable human intervention and continuous learning. These capabilities enable downstream applications in- cluding generating shaped rewards for reinforcement learning and producing corrective task plans for recovery policies (Duan et al. (2025). While there exist many approaches tackling failure detec- tion, ranging from classical methods (Ye et al. 2019: Garrett et al. 2020) to recent learning-based methods (Duan et al. 2025), they primarily locus closed set of failure modes. In reality, failures in robotics are often nuanced, combinatorial, and difficult to enumerate exhaustively. This complex- ity calls for vision- languaee models (VLMs) that can leverage broad visual knowledge reason about novel failure modes in open-ended natural language, rather than task-specific models limited to predefined failure taxonomies. To address these challenges, our work fine-tunes VLM with custom prediction heads to jointly tackle failure detection and open-ended failure reasoning.
However, training good failure reasoning model is challenging due to data heterogeneity: while binary failure labels can typically be obtained automatically from system logs, detailed reasoning labels are costly and time-consuming to collect. Recent work AHA (Duan et al. (025 tackles failure reasoning in robotics by applying supervised instruction fine-tuning (SFT) (Liu et al. 2023a) on a curated dataset of simulated failures with complete reasoning annotations. While effective in this restricted setting, where both binary and reasoning labels are always available for all episodes,

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Failure Mode: Spillage?
Failure Mode: Unstable Grasp?
ARMOR
Detection
Reasoning
Failure: Yes
bag picked up...
Refinement
Failure: Yes
from the bottom
T-2
Failure: Yes
The robot picknd up the bottom, cousing the items inside spill out...
T+N
Closed-set failure
Open-ended iterative reasoning beyond fixed categories
classification
Figure I: We present ARMOR: adaptive round-hased multi-task model for robotic failure detection and reason- ing. Prior approaches reduce failure reasoning closed-sct set classification of pre-defined modes (e.g.. "spillage" "unstable grasp"). In contrast, ARMOR performs open-ended, iterative refinement jointly refining its detection and reasoning predictions. This enables accurate detection with nuanced and human-like reasoning that capture real-world failures beyond fixed categories.
this approach suffers from data inefficiency in our heterogeneous data regime. Furthermore, AHA's evaluation relies hand-crafted regular expressions, making correctness sensitive ad-hoc format parsing. As shown in our experiments, also fails to generalize beyond the predefined failure modes is trained on. In contrast, we introduce novel fine-tuning algorithm based multi-round refinement, which scales to heterogeneous supervision and supports open-ended reasoning across diverse and nuanced failure scenarios.
Recent advances in improving foundation models' reasoning capabilities with iterative refinement have shown strong results in domains such as math problems and coding Madaan et al. (2023): Zeng et al. (2023); ; Qu et al. 2024); Yuan et al. (2024). To our knowledge, we are the first to extend this research frontier to multi modal failure detection and reasoning with high dimensional video input. In practice, however, naively treating failure detection and reasoning single free-form generation task limits classification accuracy and produces inconsistent outputs, as seen Appendix C To address this challenge, we present our method, A ARMOR: Adaptive Round-based Multi-task mOdel for Robotic failure detection and reasoning. pictorial illustration of our method compared to prior works is shown in Figure ARMOR treats failure detection and reasoning task refinement problem. At each step, the model predicts both classification outcome and a natural language reasoning, conditions its previous outputs, and iteratively improves its predictions. Our training algorithm combines expert supervision with online imitation learning (Ross et al. 20116), leveraging large-scale sparse labels for detection and smaller dense annotations for reasoning, each optimized with task-specific objectives, At inference time, ARMOR generates multiple refinement trajectories and selects the most confident prediction using metric based on self-certainty (Kang et al. 2025), enabling accurate detection together with open ended, human like reasoning.
round-based multi-
In summary, the following the key contributions of this work: We propose novel approach, ARMOR, that can handle heterogeneous data with open-ended iterative reasoning to perform failure detection and reasoning.
2) To the best of our knowledge, we are the first leverage iterative reasoning with VLM on multi-modal failure detection in robotics using video feed. 3) Experiments across diverse environments show that ARMOR achieves state-of-the-an performance by improving over the previous approaches by up to 30% on failure detection rate and up to 100% in reasoning measured through LLM fuzzy match score, demonstrating robustness to heterogeneous supervision and open-ended reasoning beyond predefined failure modes.
RELATED WORK
2.1 DETECTING AND REASONING OVER FAILURES IN ROBOTIC MANIPULATION
Failure detection is crucial for monitoring robot execution progress, with research spanning domains
from Human-Robot Interaction (HRI) Ye et al. 2019 Khanna al. 2023) Task and Motion
Planning (TAMP) (Garrett et al. 2020). Recent advances in LLMs (Hurst et al. 2024 Touvron et al.)
2023 Yang al. 2025) and VLMs (Bai al. 2025) have enabled the application of foundation
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models to visual failure detection and reasoning. Liu al. (2023b); Zhou al. (2025 leverage LLMs for failure detection by abstracting visual imput into symbolic space or code, but this approach
risks losing fine-gratn inneggrined visual cues necessary for detecting subtle failures. Other works rely on off-
the-shelf VLMS without any fine-tuning, which limits performance (Skreta et al. 2024 Guo et al. 2024). Finally, prior approaches that fine-tune VLMs for failure detection and reasoning (Duan al.
2025 Du et al. 2023b) assume a fixed, pre-defined set of failure modes, essentially reducing the task to classification, In contrast, our work focuses on failure detection as a classification problem, and failure reasoning as an open-ended language modeling problem, enabling both accurate predictions and fine areegraiddd, human-like reasoning.
2.2 ENHANCING THE REASONING CAPABILITIES OF FOUNDATION MODELS
Prior research has explored enhancing the reasoning capabilities of foundation models through var-
ious techniques. These include designing prompting strategies for tool interaction (Tihanyi et al.)
2025 Wang et al., 2023 Gao et al. 2023). sequential refinement of predictions (Chen et al. 2023]
Gou et al. 2024|] ( Zhang et al. 2023) thought verbalization (Nye et al., 2021 Wei et al.) 2022 Zhou
ctt al. al. 2022 Du al. 2023a). While some methods enable self-correction, they often require start- ing with a strong reasoning model, which is often proprietary (Zhou et al. 2024 Qu et al. 2024).
2024). and self critiuci (Madaan et al. 2023] Shinn et al. 2023) or multi-agent critique (Bai
To address this, several works have employed fine-tuning to enhance LLMs seff-improvement capa- bilities (Zeng et al. 2023 Schick et al. (2023.. These approaches leverage preference learning (Ros- set et 2024). adversarial training (Chen et al.) 2024), and reinforcement learning (Yuan et al. Qu et al. 2024) to learn from -efeeeeedd responses. Most closely related to our work RISE (Qu al.J 2024). as both methods propose a multi-turn self-refinement algorithm and use on-policy data for policy optimization. However, we differ both in setting and algorithm: unlike RISE, we do not assume access to an oracle reward model during training or inference. Instead, we leverage a limited amount of instruction data with training objective resembling online imitation learning [Ross et al. 2011 and we rely on the model's 's internal signals for refinement at inference.
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2.3 LEVERAGING VISION-LANGUAGE MODELS FOR CLASSIFICATION
VLMs can be broadly categorized into discriminative models, such as CLIP (Radford et al. 2021 and SigLIP (Zhai al. 2023), and generative models (Bai al. 2025 Liu al. 2023a Wang al.
2022). depending on whether the model outputs a representation or generates text or images (Ramesh) al. 2022). Prior work has shown that fine-tuning discriminative VLMs achieves state-of-the-art accuracy on a variety of benchmarks for both in-distribution (ID) and out-of-distribttion (OOD)
data (Addepalli et al. 2024, Goyal al., 2023 Kumar et al., 2022). However, discriminative VLMs cannot produce open- onen-ended textual reasoning, making them unsuitable for our task. Recently, al.] (2024b) demonstrated that generative VLMs can perform on par with, or even surpass, state- of-the-art discriminative VLMs when fine-tuned with classification data, indicating that generative VLMM also encode equally meaningful representations for classification. Building on this insight, work fine-tunes generative VLM (Bai et al.) (025) for multitask learning-solvingg failure detection via classification and failure reasoning via text generation.
3 PRELIMINARIES AND PROBLEM SETUP
3.1 FAILURE DETECTION AND REASONING AS A MULTI-TASK MDP
Inspired by RISE Qu et al. 2024), we define a multi-task MDP M = (S,A, for our failure detection and reasoning problem. Here, S denotes the state space, A the action space, R [Ratteet Rresson } the reward functions for detection and reasoning, and T the horizon (number of refinement rounds). "Multi-task" in our setting refers to the fact that each action jointly produces two predictions-fii failure detection and natural language reasoning caatii than separate tasks with independent policies. State s, consists of input video x, prior detection and reasoning predictions (25-1 (21-). and auxiliary prompt p. Action a, consists of the current multi-task predictions (7', ee),
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(a) Failure Data
Multitask Sequential Training
Offline Imitation
Online Refinement
x
P e Z
P
i
VLM
Video
VLM
Failure: Yes
robot picked up the book, but the cover of the book fall aff...
è
Sparse data
Dense data
(d) Iterative Self-refinement Inference
(b) Multitask Prediction
Trafine
P
Mlyy (n)
Detect (1)
xattn
<CLS>
BCE
VLM
(2 222))
Tearine Tretine 10
m
Entropy
Video
VLM
Reason (c)
NTP
Videox
(lasy: e(a))
Texton Tappr
Figure 2: Overview of ARMOR. (a) Our failure data consist of heterogeneous supervision, with large-scale binary detection labels and scarce free-form reasoning labels. (b) vision-language model (VLM) with multi- task heads jointly predicts delection
classification head and reasoning e via a language decoder. trained with binary cross-entropy (BCE) and next-token prediction (NTP) losses. (c) We fine-tune the VLM with both offline imitation and online refinement: the model conditions on dataset labels (1, e) or its prior predictions (i, é) as well as auxiliary prompts to generate a new round of outputs. In online refinement, this process is repeated times. The model's predictions (denoted by and purple rectangles) are supervised with available labels. (d) At inference, ARMOR performs iterative self-refinement, rolling out multiple stochastic trajectories. select hest prediction m² from the final predictions with the lowest entropy score.
and the MDP has the following initial condition and transition dynamics:
8g = [z. 2, 23 Ø
T(8+41 8t, a) = 8(s+11 ee
The multi-task self-refinement process is a sequential decision process where the policy refines fail- detection and reasoning results for T rounds. At each round t E 10, T - 1), it predicts the answer given predictions from the previous round (?S,e") T- (-1 [ar, 1'11 e pl). Our goal is to fine-tune a vision- laiiunla model (VLM), Te. to produce human- like reasoning while achieving high failure-detection accuracy after T rounds of refinement. Our reward functions Rdetect Breason are defined as the correctness of prediction for a particular task compared to the ground-truth la- employ different metrics to approximate the correctness when computing
bels (1, e). In practice, performance, We measure detection quality with binary accuracy and reasoning quality with LLM fuzzy match score and Rogue-L score (detailed in Section
3.2 FAILURE DETECTION AND REASONING WITH HETEROGENEOUT SUPERVISION
We consider a heterogeneous supervision setting with two datasets: a large-scale sparse dataset with binary labels, and smaller but richer dense dataset with detailed human annotations:
D sparse = (x), 4 E ('success", 'failure'),
D (z,,4,,)))
is detailed failure reasoning.
This setting is realistic because binary success/failure labels often be obtained automatically (e.g., from robot sensors or system logs) and thus scale easily, whereas natural-language reasoning labels require expert annotation and far more costly. In contrast prior work AHA (Duan et al.) 2025), which assumes dense supervision for both tasks, we operate under a heterogeneous supervision setting, with large-scale binary labels and only a small fraction of reasoning labels, reflecting the practical imbalance faced in real robotic deployments.
4 METHOD
We present ARMOR, which stands for adaptive round-based multi-task refinement vision- language models (VLMs) for robotic failure detection and reasoning. ARMOR tackles the dual
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Algorithm 1 ARMOR Training
Require: VLM policy TT; Dspasse Deense; rounds T; task prompts {p}
Phase I: Offiine Imitation
[x, 2, 63, (() Dsparse Deense with BCE for NTP for 2: Expert-conditioned: Train (?), "el)ee))) (0))~T(( To(- (-1 [x. 1, plp Duense with same losses.
1: Warm-up: Train e"),e")= - :t(:
Phase II: Online Refinement
3: for mini-batches (x, from D Dsparse U dense do
4:
(1)ee)) e (2, L+ --0 do
5:
for 1=
6:
(1', (1",e") To( 10,1t-1,0 e,t-1,110 ++= BCE((", NT) Ddense]
7:
8:
end for
9:
0 + 0 VVL
end for
challenge of (1) combining classification--tylee detection with open-ended reasoning, and (2) refining predictions over multiple rounds. To this end. we introduce a model architecture with speciffc prediction heads (Section 4.1). We then cast failure understanding as a multi-task sequential predic- tion problem and train VLMSs to condition on prior outputs from each task (Section (42). Finally, we design an iterative inference procedure that leverages entropy- baarep confidence to refine and select An overview of our method is shown in Figure
final predictions (Section 4.3
4.1 FAILURE DETECTION AND REASONING VIA MULTI-TASK PREDICTION
Prior work such AHA (Duan et al. 2025 treats failure detection and reasoning as a single lan- guage modeling task, where the model outputs free-form text that consist of both the binary outcome and short explanation. Evaluation requires hand-crafted regular expressions to extract each answer. This design couples two inherently different objectives and leads to optimization difficulties, since binary classification is optimized through language modeling loss, and to evaluation fragility, since correctness depends on ad-hoc format parsing rather than explicit comparison.
We instead introduce multi-task architecture with separate heads for detection and reasoning. Con- cretely, given a VLM backbone consisting of vision encoder and language model decoder (e.g.. Qwen2.5-VL (Bai al. 2025)), we follow the established practice (Devlin et al., 2019 Radford et al. 2021) and attach a lightweight classification head to the intermediate decoder representation to produce a [CLS] token for detection, trained with binary classification. The original language model head is retained for reasoning generation, trained with next-token prediction. Both heads share the same vision encoder and language decoder parameters, ensuring consistent representa- tions while allowing task-specific optimization. This separation removes the need for brittle answer extraction, as each prediction head will receive a task-specific input prompt and predicts only task- relevant output. It also enables better supervision under our mixed dense-sparse data regime: dense data provide full labels for both tasks, while sparse data supervise only detection but still encour- age reasoning generation context. The result is model that optimizes each task appropriately while allowing their outputs to inform one another. Details on model architecture and task-specific prompting in Appendix A and B.4
4.2 TRAINING VLMS ON MULTI-TASK SEQUENTIAL PREDICTION PATHS
Compared to formulating failure detection and reasoning as a single task of generating free-form natural language, the proposed multi-task task heads greatly improve failure classification accuracy. However, failure reasoning quality does not improve and the two heads may output inconsistent results. Two challenges to foeus and the reasoning capability of the model. To address this, ARMOR employs a novel multi-task self-refinement process where failure detection and reasoning iteratively refined. At each round t E {0, 1,..,, T' -1--1 1}. the policy predicts the next round's answer given previous detection and rea- soning predictions (I*, e²) ~ no [z. 1x- 1-1 et p'l). Unlike Qu et al. 2024)) we do not assume access to external reward function. Instead, directly optimize correctness indicators against
improving the consistency of the output of the two heads
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Algorithm 2 ARMOR Inference
Require: Trained my; input æ; maximum rounds Trefine tolerance e; samples M; task prompts {r/}:
weight
Init: For all E {I,...,M) set (fom) (m)) (0, R3); Cuin t 20
2: for 1 to T refine do
3:
for m - t0 M do
4:
((m))
To [x, (m)) x-1
{(m):Pp)) p'])
5:
H(m) t
Entropy( (T [2, (im)= (mm p'l) (m))
Help) Mean MeanTokenEntroopp (T [a,, (m)= ( (m))
6:
C(m) H((m) + A (XH)) end for
7:
8:
m" f- arg minm C(m)
9:
10:
If I(m)" 2 Cuuip = e, break
11:
Else Ciin t- C(m²)
end for
13:
return Ei, - (mmry ((mm)y) (mmr))
available supervision:
max
E(x.))-D Eeet (t) [a, - [1(1' =(]
(1)
8
sparse supervise detection only
+ (x,de))) Drave Eect S( ) (:1 [a, 1,et 11))) [1(1 =) + L(e' e)]
(2)
dense data: supervise delection & reasoning
During training, we construct sequential decision paths using both the sparse dataset D sparse and the dense dataset Déese to fine-tune the VI.M. Our training procedure consists of two phases: offline imitation and online refinement, as detailed in Algorithm
Offinne Imitation. We begin by warm-starting the VLM using both sparse and dense datasets. In the first stage, the model is fine-tuned to predict detection and reasoning outputs without conditioning on any prior rounds, i.e., (L" e²) ~ T- [z,, Ø, Ø Ø)). This provides a baseline initialization where supervision is applied directly to the first-step predictions. In the second stage, we leverage the dense supervise predictions conditioned on the expert outputs, (12 e²). ~ TT( [zr, [.,,,,e.p') which encourages the model to preserve correct prior predictions and maintain cross-task consistency. In practice, for each sample we randomly select one task to train and mask out its ground truth input while retaining other inputs. This prevents the model from copying the task input and allows it to focus on cross-task and visual information.
dataset provide conditional expert transitions. Specifically,
Together, these two stages ensure that the model learns to produce valid single-round predictions and to align its outputs with expert demonstrations when conditioning on prior task results.
Online Refinement. To address the mismatch between expert demonstrations and the model's own behavior, we further fine-tune the model with online rollouts from the policy. For each training example, we roll out the policy for T rounds. Losses are computed against available supervision: detection is always supervised (on both Depars and Ddense), while reasoning supervised only on D On Deparse reasoning is generated but not penalized. This procedure allows the model to refine over its own trajectories while leveraging the ground truth whenever available. For both offline and online imitation, detection is trained with binary cross-entropy (BCE) loss, and the reasoning is trained with next-token prediction (NTP) loss.
4.3 ITERATIVE RENTNEETIN FOR INFERENCE
At inference time, ARMOR generates multiple refinement trajectories and uses entropy self- certainty metric (Kng et al. 2025), enabling accurate detection together with open-ended, human- like reasoning. Starting from (10) e) (2,0), for each round t and trajectory m, the model predicts
((m))((m)) ) ~ TO (.11x,
~ ( (()) ) x1)

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We compute detection entropy Haa and reasoning entropy Hreason conditioned prior outputs, and form a combined entropy score:
mL)
Hel
H(
We run refinement until the best trajectory no longer reduces uncertainty, i.e., when its entropy score stops decreasing beyond a tolerance Entropy serves a crucial role in this process: it determines when refinement terminates and also guides sample selection by identifying the most confident tra- jectory. The final output (i, é) is taken from the trajectory with the lowest entropy score (highest confidence) termination. A detailed algorithm for inference is provided in Algorithm 2
EXPERIMENT RESULTS
evaluate our method against baselines across four diverse failure datasets spanning household and industrial warehouse environments. Our goal is to answer the following research questions:
1. Does ARMOR improve failure detection accuracy over prior works?
2. Does ARMOR offer better failure reasoning over prior works?
3. Does ARMOR work when sparse labels come from a different environment?
4. How does each component of ARMOR affect performance?
5. How does refinement improve the model's prediction over time?
Table 1: Quantitative Results on Failure Detection and Reasoning. Metries include detection accuracy and reasoning quality (LLM Fuzzy and ROUGE,). Our method achieves higher perfor- mance across different domains, accurately detecting failures and producing high quality reasoning.
Models
Evaluation Datasets
Detect Acc.
LLM Fuzzy + ROUGEL
RLBench
0.376
0,255
0.353
Sparrow
0,453
0,240
0.137
Qwen2.5-VL
Maniskill
0.548
0.112
ARMBench
0.500
0.349
0.208
RLBench
0.317
0,220
0.140
Sparrow
0,510
0,346
0.311
Cosmos-Reamt Reasoning
Maniskill
0.442
0.359
0.207
ARMBench
0.480
0,503
0.480
RLBench
0.067
0.032
Sparrow
0,500
0.008
0.078
LLAVA-NeXT
Maniskill
0.042
ARMBench
0.500
RI.Bench
0.420
0.372
0.336
Sparrow
0.517
0.213
0.138
Claude-3.7
Maniskill
0.538
0.360
0.133
ARMBench
0.590
0.494
0.387
RLBench
0.561
0.473
0.526
Sparrow
0.650
0.407
0.458
Claude-3.7 (3-shot)
Maniskill
0.625
0.398
0.2112
A ARMBench
0.650
0.685
0.725
RLBench
0.640
0.460
0.606
Sparrow
0.523
0.278
0.274
SFT-D
Maniskill
0.788
0.644
0.743
ARMBench
0.640
0.609
0.618
RLBench
0.726
0.550
0.646
Sparrow
0,620
0.245
0.257
SFT-S+D
Maniskill (R-M)
0.490
0.177
0.381
ARMBench (S-AA)
0.495
0.007
0.249
RLBench
0,917
0.718
0.802
Sparrow
0.733
0,503
0,514
ARMOR (Ours)
Maniskill (R-M)
0,990
0.673
0.851
ARMBench (S (S-A)
0.725
0.698
0.721

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Table 2:
Ablation of ARMOR's components, Each row shows the impact of different stages on detection accuracy and reasoning quality. ARMOR combines all components to achieve the best overall performance.
Offine Warmup
Oftinee Expert
Online Imitation
Refinement (Inference)
Ablation
Detection Reasoning
Condition
Multitask Prediction
X
x
0.897 /0.460
Refinement Only
X
0.803 / 70.488
Offiine Imitation Only
7
x
0.853 7 70,658
Online Imitation Only
X
x
/
0.850 / /0.683
ARMOR (ours)
0.917 70.718
Datasets. We evaluate on four datasets across different domains. The first two come from the AHA paper (Duan et al. 2025): RLBench-Fail Fail and Maniskill-Fail. These contain simulated tabletop manipulation tasks such as "putting the trash in the bin" or "inserting the peg in the hole," where failures are induced by perturbing a scripted policy with specific failure modes (e.g., "the robot did not close its gripper"). Following AHA's official codebase, we generate the failure datasets and split them into dense labels (with failure modes) and sparse labels (without). We refer readers to the AHA paper for further details. Sparrow-Fail and ARMBench (Mitash et al. (023) are from real warehouse operations; both are collected with robotic manipulator transporting objects between containers with heterogeneous contents. Failures in these datasets are more diverse and nuanced, with sparse labels obtained from system logs and dense labels from human annotators.
(Bai et al., 2025). Cosmos-Reasoning (Azzolini et al.) 2025), and VL-VA- (Zhag et al. 2024a) as well as one proprictary model, Claude-3.7 (with few-shot prompting). For fine-tuning baselines, following prior work (Duan al. 2025), evaluate supervised fine-tuning on dense data only (SFT-D), on both dense and sparse data (SFT-S+D). Unless otherwise specified, all open-source VLMs and ARMOR use 7B model variants.
Baselines. We compare against three state-of-the-arr open-source LLMMs--Qen2.5-V
Metries. We report Binary Success Rate for detection, LLM Fuzzy Match (semantic similarity) and ROUGE-L (longest common sequence) for reasoning. Differ from AHA (Duan et al. 2025). we compute the LLM fuzzy score only on the reasoning part to prevent detection result from affecting reasoning metrics. Details on the datasets, baselines, and metries are provided in Appendix
5.1 FAILURE DETECTION AND REASONING PERFORMANCE
[QI & Q2] Table shows that ARMOR achieves the highest scores in both binary detection and rea- soning quality across all datasets. Prior fine-tuning approaches suffer from the dataset imbalance: SFT-D only leverages the limited dense data and produces suboptimal results, and SFT-S+D treats dense and sparse data as the standard next token prediction task, which often causes the model to overfit to the sparse data while failing to produce meaningful reasoning, leading to inferior perfor- mance. Open-source VLMs such as Qwen2.5-VL and Dossoss-Resoninn cannot perform beyond random guessing (50% accuracy), suggesting that generic instruction-turn tunded VLMs requires align- ment for failure reasoning. Proprietary VLMs like Claude achieve stronger overall performance, especially with few-shot prompting, but still fall short in performance to ARMOR in most tasks.
[Q3] We combine sparse data from Bench and dense data from Maniskill and evaluate in Man- iskill (R-M) to study small distribution shifts, where they differ in embodiment and workspace setup. For large distribution shifts, we combine sparse data from Sparrow and dense data from ARMBench (S~A), where they differ in both workspace setup and failure reasoning. In R-M, ARMOR improves over SFT-D by +25.6% in detection and maintains high reasoning quality. SFT- S+D reasoning collapses from 0.644 0.177. showing that naively mixing dense and sparse data prevents generalization. In the harder S-A setting, ARMOR outperforms SFT-D by +13.3% in de- tection and +14.6% % reasoning, and substantially exceeds SFT-S+D. These results show that our model consistently improves both detection and reasoning quality across multiple reasoning metrics.
5.2
ABLATION STUDIES
[Q4] We consider the following ablations to our method evaluated in RLBench. Multitask Pre- diction employs two heads for prediction and only fine-tunes unconditioned offline data. This isolates the benefit of our multi-task architectural design without conditional training or refinement. Refinement Only uses ARMOR's 's architecture to only perform self-refinement at inference, remov- ing training (except for warm-up) from our algorithm. Offline Imitation removes training on online transitions, while Online Imitation Only only trains online transitions. All ablation models
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Entropy
Combined Entropy
11h/e
0.126
0.125
0.8
Perfor nance
0.124
Reasóning Enirooy
0.123
0.2
0.122
0.121
c4c4
Refinmmen, Found
Combined Entropy (meen over samples)
Detection Pe Performence toest sample]
Reesoring Performence Ibest sample)
(a) Effect of refinement rounds entropy and performance.
Prempt:
attempting to transport object
successfully
transport the object?
Round
Round 2
Round 3
ARMOR Refinement
Failure: Yes
Failure: Yas
Failure: Yes
The robot picks an item with excessive force, visibly deforming or damaging at the grip point.
The robot picks up an item, but the item's integrity is compromised, with visible changes its structure. This might an item contents.
The robot successfully picked up the box and dropped it in the target bin.
spilled its
(b) Example of ARMOR's 's multi-round refinement process.
Figure 3: Refinement analysis in ARMOR. Effect of refinement rounds on entropy and performance: de- lection reasoning entropy decrease steadily across rounds, while combined plots show that refinement improves both detection accuracy and reasoning quality (values computed over 300 test datapoints). (b) Ex- ample of ARMOR's 's multi-round refinement process, where predictions are iteratively updated improve the quality and consistency between detection and reasoning outputs.
trained for the same total number of epochs as ARMOR. When an ablation excludes certain training stages, add additional epochs the remaining stages ensure fair comparison. We visual- ize each ablation's 's training stages and their performance in Table 2 Multitask Prediction already achieves strong detection accuracy (0.897) but suffers from poor reasoning quality (0.460), con- firming that architectural design alone is insufficient. Refinement Only slightly improves reasoning (0.488) but reduces detection accuracy (0.803), suggesting that multi-task conditional training necessary. Offline Imitation and Online Imitation both improve reasoning quality compared to the previous variants, with online data contributing more strongly to reasoning. Finally, combining all components in ARMOR yields the hest performance, highlighting the benefits of multi-task design, refinement, and offline and online training.
Number of Refinement Rounds. We ablate the number of refinement rounds and track both performance and entropy using test data in RLBench. We run inference with 4 seeds and report the mean and standard deviation for each refinement round in Table 3 This demonstrates that the refinements consistently improve performance with decreasing variance.
In Figure Bal entropy for detection and reasoning decreases across rounds and stabilizes during refinement (left), while the combined entropy C. along- side task performance (right) shows that detection accuracy improves with additional refinement, and reasoning exhibits significant gains after the first round. Together, these results confirm that ARMOR's iterative refinement re- duces uncertainty and enhances both detection and reasoning quality. We provide additional ablation study results in Appendix C.1
Table ARMOR's reasoning performance across refinement rounds. Mean and std are reported across 4 seeds. Each round shows improvements with decreasing variance, demonstrating consistent gains.
Refinement Rounds
Reasoning (LLM Fuzzy)
Round
0.475
Round 1
0.676:10.025 (+42.4%)
Round 2
0.703
Round
0.71740.002 (+2.0%)

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5.3 REFINEMENT PROCESS
We first address [Q5] by showing that performance improves over refinement rounds in Figure Next, we provide qualitative results in Figure 3b] showing how ARMOR iteratively improves its predictions. In the first round, the model produces incorrect reasoning while correctly detecting the failure. In later rounds, it first adjusts the reasoning to stay consistent with the detection result (failure) and then refines it into accurate account of the failure. This process demonstrates how multi-round refinement reduces inconsistencies and enhances reasoning quality. In general, we ob- serve that reasoning results tend to be corrected based on detection, since detection is generally more reliable (as more data is available for detection), though there are also cases where both predictions are corrected through successful refinement. Additional examples are provided in Appendix
Ba
5.4 INFERENCE COST
We evaluate the computational overhead of ARMOR's ' refinement process on 8xA100 GPUs (40GBs of memory each) using a batched implementation where multiple rollouts increase only memory usage, not inference time. Table 4 shows memory and time usage across refinement rounds for single question, where round 0 refers to the base model's (Bai et al. 2025) latency. ARMOR adds minimal computational overhead compared to the base model approximately Is increase in wall clock time per round, with modest memory overhead (from 3.4 3.48GB to 6.31GB per GPU). To balance performance and efficiency, one can choose to use fewer rounds of refinement (e.g.. round refinement already improves reasoning by 40%).
Table 4: Memory usage and wall clock time across refinement rounds. ARMOR adds minimal computational overhead, with approximately Is increase per round and modest memory growth.
Refinement Rounds
Avg. Memory Usage per GPU (GB)
Wall Clock Time (s)
Round 0
3.48
7.95
Round
5.84
9.30
Round
6.12
10.50
Round 3
6.31
10.95
6 CONCLUSION
We introduced ARMOR, new method for robotic failure detection and reasoning based on itera- tive multi-task refinement. ARMOR learns from both sparse labels and dense annotations, handles heterogencous supervision, and extends VLMs open-ended reasoning beyond predefined failure modes. Through experiments across simulated and real-world environments, we highlighted that ARMOR achieves state-of-the-art performance in both detection and reasoning. For future work, we plan to incorporate richer training signals such as task rewards and human preferences to further enhance reasoning quality and alignment with human. We also plan extend ARMOR other syn- ergistic tasks, such as predicting safety violations and recovery strategies, which could enhance its applicability in real-world robotics. Finally, integrating modalities beyond vision, including force- torque sensing and proprioception, may capture wider distribution of failures in the real world.
Broader Impact. Robotic failure detection and reasoning plays critical role in enabling au- tonomous systems to operate safely and effectively in real-world environments. While binary failure detection alone may suffice for controlled settings with known failure modes, our work focuses on failure reasoning, which can provide interpretable feedback for robot correction and human interven- tion. This is essential is crucial to improve robot policy and reduce the need for expert supervision during deployment. Furthermore, manipulation tasks increasingly involve diverse objects, envi- ronments, and failure modes that benefit from the generalization capabilities VLMs. As VLMs continue to advance, they hold promise for enabling more generalizable and interpretable failure reasoning in autonomous systems, and our work contributes to this direction by demonstrating how to effectively leverage heterogeneous supervision for better performance.
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ETHICS STATEMENT
We have adhered to ethical research practices and legal standards. Our study advances methods for robotic failure detection and reasoning using video datasets. We use simulated or warchouse- collected data without personal information. The research aims to improve the safety, transparency, and reliability of autonomous systems, aligning with the principles of responsible stewardship and minimizing potential harm. We acknowledge that over-reliance on imperfect reasoning models in safety-critical settings may pose risks, and emphasize that our method should complement, not replace, rigorous monitoring and human oversight.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, include detailed descriptions of the implementation of our algorithm in Appendix A datasets Appendix [B.]] and full prompts and evaluation protocol in Appendix B.4 We will release open-sourced datasets and code upon publication.
ACKNOWLEDGMENTS
The project was developed during CQ's internship Amazon Roboties. AZ and CQ were funded by NSF 2340651, NSF 2402650, DARPA HR001 12490431, and ARO W911NF-24-1-0193.
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Appendix
THE USE OF LARGE LANGUAGE MODELS (LLMS)
We have used LLMs for polishing the writing of the paper. We have also employed LLM to compute one of the evaluation metrics (LLM Fuzzy Matching, detailed in Appendix [B.3). LLM does not serve major contributor this paper, all of the ideas are developed and implemented by human authors.
A IMPLEMENTATION DETAILS
A.1 MODEL ARCHITECTURE
We Qwen2.5-VL Bai et al. 2025) as the VLM backbone for ARMOR. For detection, attach a lightweight binary classification head. The head takes mean-pooled features from the first four LM decoder layers, projects them, cross-attends with learnable [CLS] ] token, and decodes with an MLP to produce logits. Reasoning is generated using the base model's s auto-regressive decoder. Both heads share the same vision encoder and LM decoder parameters. Conditioning is injected through prompts such as Giiven the previous detect ion is ...'. or ''i ven the previous reasoning is ... ensuring that each round depends the prior outputs. We provide schematic of the architecture in Figure4anne pseudo-code for the classifier head below:
class ViClassifierHedt (nn. Module):
def
hidden_ sedddensiize-096 num_classes-2, dropout=0.1)
super () ()nanitt___._ init_())))))
self.cls_tcken Parameter sterch.zzeros, 1, hi hidden_size)
Feature projection
self. projecrrr nn Sequential
nn. Liner (hidden_size, hidden_size), Gn.GELU nn.rropout (dropout), Lineir (hidden_see size, hidden_ size)
Aggregation vía attention self.agg_ater nn. embed_dimee babbo__dimbidden_sise num hum_heeds=8, patch_first-Pe
Output classifier MLP
self.out mlp Sequential nn Linerr (hidden_size, hidden_size), Gn.GELU() (), nn. Dropout (dropout), Linein (hidden_size, hidden_size // 2),
Gn.GELU nn. Dropout (dropout), Linear (hidden_ size // 2, num classes
def forward (self, features):
features Seatures.size(0 (0)e cls sel
-1, -1)
features self 1n (se. (features)) attn, self agg (c1s, features, features) )1)
return self. out. mlp
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A.2 TRAINING AND INFERENCE
We leverage an open-source codebase (Lee 2024) to run our fine-tuning experiments. During offline conditional training, when the model is given ground-truth prior predictions el, as inputs, randomly mask out one of the predictions so that it also learns to refine from imperfect contexts rather than overfitting to the fixed-point supervision. Specifically, we first randomly select task, and then we mask out the ground truth input for that same task while retaining the ground truth from the other task. For example, when predicting reasoning, we mask the ground truth reasoning input but keep the detection input, allowing the model to leverage cross-task information along with visual context. For video-based datasets (Sparrow and ARMBench), we use a global batch size of 16, while for collaged image datasets (RLBench and Maniskill) we use batch size of 64. All models are trained for epochs for offline phase and 10 epochs online phase on 8HH100 GPUs. We use A with weight decay 0.1 and cosine learning rate scheduling with a warmup ratio of 0.03. The base learning rate for the LM decoder and classification head is set to 1 10 55 while the vision encoder is trained with smaller learning rate of 2 x 10-6, For refinement, to place greater weight on detection entropy, since the detection head benefits from supervision on both sprore and Dense and is thus more reliable than the explanation predictions. We summarize the hyperparameters in Table 5
set À 0.1
Table 5: Hyperparameters for training and inference.
Hyperparameter
Value
Global batch size (videos)
Global batch size (collaged images)
64
Offiine epnchs
3
Online epochs
Horizon T
3
Frames second (FPS)
5.0
Learning rate (LM + classifier)
1 x 10-5
Learning rate (vision encoder)
2 x 10-6
Weight decay
0.1
Warmup ratio
0.03
LR scheduler
Cosine
Dropout
0.1
Refinement weight
0.1
Refinement Samples M
3
Max Refinement rounds T Time
Entropy tolerance Temperature Top P Number of beams
x 10
0.7
None
B EXPERIMENT DETAILS
Dataset
Domain
Sparse
Dense
Test
Unseen task
Unseen workspace
Unseen failure
RLBench
Simulation
8000
1000
300
/
Maniskill (R + M)
Simulation
0
600
100
/
Sparrow
Real world
3000
600
300
ARMBench (S - A)
Real world
0
600
200
X
Table 6: Details of datasets. Dataset domains, number of sparse and dense samples used for train-
ing, and types of distribution shifts between train and test data.
B. DATASETS
T an overview of dataset sizes and domains. Below we describe each dataset and its generation procedure in detail.
RLBench (James et al., 2020) is a simulator on top which AHA Duan et al. 2025) developed
wrapper generate set of predefined failure modes. The implementation contains diverse tasks
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Success/Falture
The rabot damaged the objact during the manipulation
LM Decoder
Original Prompt
Given the prewipus KTzaskk
<prediction
Vision Encoder
Figure 4: ARMOR model architecture. We select the intermediate layer representation from the LM decoder of Qwen2.5-VL. (Bai et al. 2025) and attach classification head for detection, while using the original LM decoder for reasoning. Conditionnng prompts describe the previous outputs for each task, enabling iterative multi task refinement.
such as "putting the trash in the bin" or "inserting the peg in the hole," where failures are induced by perturbing scripted policy with specific failure modes (e.g.. "the robot did not close its grip- per"). Following AHA, we construct input images by combining multiple frames across different time steps and camera views into matrix: the top row shows the front view, the middle the wrist view, and the bottom the overhead view, with timesteps annotated left to right. Using AHA's code, we generated our own RLBench dataset across 10 tasks with 8000 sparse samples and 1000 dense samples. We evaluate on three unseen tasks.
Maniskill (Mu al., 202T) is a simulator for table-top manipulation, also used in AHA. This dataset differs from RLBench in its tasks and robot embodiment (Sawyer robot). We follow the same multi- view image construction protocol as RLBench and generate 600 dense samples for tasks, which are combined with the sparse samples from RLBencc to create training dataset for the transfer setting (R + M).
ARMBench (Mitash al., 2023 is collected in a real warchouse with robotic manipulator trans- porting objects between containers with heterogeneous contents. Failures include object defects such books opening items spilling their contents. We directly the video feed resized to 320 x 320 and sampled at FPS. Sparse samples are obtained from system logs, and dense samples provided by human annotators. We use 600 dense samples for training and combine them with sparse samples from Sparrow to create a dataset for the transfer setting (S + A).
Sparrow custom dataset similar ARMBench but differs in workspace setup, bin locations, and camera angles. It also contains more diverse failures and annotations. As with ARMBench, we use the resized 320 x 320 video feed sampled at 5 FPS. Failures are annotated in more nuanced ways, with sparse samples obtained from system logs and dense samples from human annotators. We use 3000 sparse samples and 600 dense samples from Sparrow.
B.2 BASELINES
Open-source VLMs. We compare against three state-of-the-art open-source vision-language mod- els (VLMss): QW (Bai et al. 2025). Cosmos Reasoning (Azzolini al.) 2025). and LLaVA-NeXT (Zhang et al.) 2024a). Qwen2.5-VL is a recent large-scale VLM with strong vi- sual grounding and ciarucctionfollloviii with improved chain-of- langunel model optimized for video and temporal grounding.
capability. Cosmos-Reasoning prompting for reasoning-heate tasks. LLaVA-NeXT is efficient
Reasoning extends standard VLMs
Proprietary VLM. We also evaluate Claude-3.7, proprietary model accessed through its API. We apply few-shot prompting using structured templates that encourage the model to output both reasoning and binary decision. The full prompt examples are provided in Appendix B.4 B.4
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Fine-tuning methods. Following prior work (Duan et al. 2025 include supervised fine-tuning
baselines: SFT-D: fine-tuning on dense data only. SFT-S+D: fin-tuning on both sparse and dense data jointly, where simply has empty strings between wecbink></hinne for sparse data. All models are trained for the same number of epochs (13) as our method, using the Qwen2.5-VI. al. 2025) backbone and identical hyperparameters.
(Bai
B.3 EVALUATION METRICS
We employ three complementary metrics to assess both failure detection and reasoning quality. Binary Success Rate. For detection accuracy, we compute the binary success rate by directly com- paring predicted labels i with ground-truth labels
Accuracy
Σ 2=1
144
where is the number of test samples. This provides direct measure of failure detection.
LLM Fuzzy Matching. We employ LLM Fuzzy Matching, which leverages an external language model (Claude-3.7-Somnn to judge semantic similarity between generated explanations and refer- ences. The external model evaluates whether two explanations are semantically equivalent, even when their surface forms differ.
ROUGE-L. (Ganesan) 2006) To complement LLM Fuzzy Matching, compute the ROUGE-L score to evaluate textual reasoning quality, ROUGE-L measures the similarity between a generated reasoning y and coman-annotatee reference y" by focusing the longest common subsequence (LCS). Let m and n the lengths of y and y" The score ROUGE, is defined as:
F9 (1 + 11+.).....
52.
where 3 is typically 1. This metric captures fluency and informativeness by rewarding longer, in-sequence matches rather than isolated n-grams.
B.4 PROMPTS AND EVALUATION PROTOCOL
General Prompts. For all baseline models, we apply the following prompt. The prompt explicitly
asks the VLM to provide both reasoning and binary decision in the following format:
("question_id": "0", laysten_promptt "You are observing a video showing embodied agents 4 (robots or humans) demonstration.
Please answer the question in the follewing format: <thinksyour reascning</think>
<answee>Yer/No<//sseezz
"user :user_prompt": "In the video, the robot is attempting to transport an object from onc bin to another. AnDid the robot successfully transport the object?"
ARMOR Prompts. Since ARMOR uses multi-task prediction, each task is associated with slightly different prompt, optionally including conditioning prompts from previous rounds.
###fUP** DETECTION *А//н**
In the video, the robot is attempting to transport an object from one bin to another.
Did the robot successfully transport the object? [Conditioning: "Given the previous detection is ...", "Given the previcus reasoning is ..."] Please give Yes/No answer.
###fte## REASONING #*##AA##
In the video, the robot is attempting to transport an object from one bin to another.
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Did the robot successfully transport the object? [Conditioning: "Given the previous detecticn ...", "Given the previcus reasoning is ..."! Please give your reasening.
Few Shot Prompts. For the proprietary baseline Claude-3.7, query the model through its API using few-shot prompting. The general prompt is the same for Claude and others. We additionally provide the full few-shot prompt used in evaluation below:
("question_id"; "0", prtreem_prrmpt:::: "You are observing a video showing embodied agent.s 4 (robots or humans) demonstration. Please answer the question in the follewing format: <thinksyour reascning</think>
"user_prompt": "For the given sub-tasks, first determine it has succeed by choosing from [V"yes\", \"no\"], and then explain the reason why the current sub-tasks has failed. Here examples:
Example 1:
Question: In the image robot arm perferming pick the red cube. <think>The robot succeeded at the sub-task </think> <answeryyes<annswe>
Example 2:
Question: In the image robot performing plug the white charger in the socket.
<think>The rebot succeeded at the sub-task. </think> <answer>yes<annswer>
Example 3:
Question: In the image ... robot arm performing pick up cube. <think>No, the robot gripper moved to the desired position with an offset along the y baaction.</thnll <answer>noo</nswer>
Now answer the follewing question: Question: In the image robot arm performing pick up the red cube." ")
Evaluation protocol. For ARMOR, we use the classifier head's output for detection prediction and the explanation head's output for reasoning, avoiding the need to regular expression matching for answer extraction. For baselines, following AHA (Duan al. 2025). extract the binary decision from the <answer> field to compute detection accuracy, and evaluate the free-form reasoning from the <think> field separately using LLM Fuzzy Match and ROUGE,, If the model does not follow the required output format (i.e., if the two fields cannot be extracted via regular expression matching), treat the entire output as the explanation and detection correctness false. This evaluation procedure is somewhat brittle, since it cannot distinguish between model that fails to follow formatting instructions and one that consistently outputs the wrong answer. To mitigate this issue, all of our test datasets are balanced to contain an equal number of success and failure cases.
C ADDITIONAL RESULTS
Example of inconsistent prediction from Qwen2.5-VL (Bai et al. 2025) model. The following example highlights the inconsistency: the baseline model's reasoning states that the robot success- fully transported the object, yet its binary decision is No (failure). Such contradictions common when failure detection and reasoning are treated as a single free-form generation task, motivating ARMOR's explicit multi-task refinement design.
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<think>The robot arm positioned above the blue bin, and it appears to be in the process of picking up an object. The object is not
visible in the image, but based on the robot's posture and the
context of the task, it seems that the robot has successfully transported the object from the left bin to the right
bin. <inn</think>
<answer> No </answer>
AADPCOV Ground truth NALEEPET
<think>The robot attempts to pick up an object from the source bin, but fails to successfully grasp it. I can see the suction gripper moving toward the target object and making contact with it. When the instead being liftea. I nctice the robot arm moving away without the intended item. Since the robot's task requires transferring objects, this failure prevents successful task completion. "</think> fanswer>No</answer>
gripper attempts to lift, the object remains
C.I ADDITIONAL ABLATION STUDIES
Effect of Sparse-Dense Data Ratio. We provide experiments showing performance across varying ratios of sparse dense annotations on Sparrow ARMrow-AAMMBBnch setting. We fix the amount of sparse data from Sparrow and vary the amount of dense data in ARMBench, as shown in Table 7 The results show that ARMOR robust to up to 10x data imbalance between sparse and dense an- notations (300 dense, 3000 sparse samples). However, further decreasing the dense samples causes reasoning performance to deteriorate. ARMOR scales well with increasing dense data, achieving significant performance gains when we decrease the ratio to 2 (1500 dense, 3000 sparse samples).
Table 7: Performance across varying sparse-dense data ratios. The column in parentheses represents the setting used the main paper.
Sparse Dense Ratio
2
(5)
30
Detection / Reasoning
0.813 0.831 (0.725 0.698) 0.640 0.609
Effeet of Model Size. We evaluated ARMOR using the 32B version of Qwen2.5-VL Bai et al. 2025 on Sparrow and observed consistent performance gains compared to the 7B version, as shown in Table g The results demonstrate that ARMOR'S approach scales effectively with larger model capacity.
Table 8: Performance comparison across model sizes on Sparrow dataset.
Model
Detection Reasoning
Qwen2.5-Vi. 7B
0.453 0.240
Qwen2.5-VL. 32B
0.563 0.268
ARMOR 7B (ours)
0.733 0.503
ARMOR 32B (ours)
0.765 0.562
C.2 MORE REFINEMENT EXAMPLES
Example of refinement that corrects both detection and reasoning. The following example shows how iterative refinement can recover from an initial misclassification (No) and inconsistent reasoning, converging to a consistent and correct prediction by the final round. The task execution frames are shown in Figure 5
<prompt>In the video, the robot is attempting to pick up hockey stick. Frames are shown from multiple views (frent, wrist, overhead) across timosteps. Did the robot succeed at the subtask?</prompt>
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Figure 5: Hockey-stick task from RLBench Multi-view frames (front, wrist, overhcad) across timesteps showing the robot attempting to pick up a hockey stick.
Figure 6: ARMBench transport task. The ground truth failure is item damage caused by detached book cover.
#ALIELUFE Ground truth #AAFFEUEE
<think>The rebot succeeded at the sub-task. </thnk>> <answer>Yes</nnswer>
*AALE!FFf Refinement History #ESSI!I**
Step 1:
Detection
Reasoning = The robot gripper moved with an offset along the y direction.
Step
Detection Yes
Reasoning = The robot fails to close the gripper.
Step 3 (Final Output): Detection Yes
Reasoning = The robot succeeded at the sub-task.
Example of refinement failure: refinement does not converge to the correct reasoning.
We use an example from ARMBench (depicted in Figure T to show failure of ARMOR's refine- ment: while the binary prediction is correct (No), the reasoning drifts toward alternative but incorrect explanations (collision - misplacement) and never captures the true failure (item damage). Future work could incorporate structured failure attributes (e.g., "collision." "misplacement," "damage") as intermediate supervision in the form reward signal to better constrain the model's 's reasoning.
<prompt>
In the video, the robot is attempting to transport an object from one bin to another. Did the robot successfully transport the object?
</prompt>
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MADPPPPE Ground truth AASEEPEEE
<think>I can see visible damage to the item during the robot's 's handling process. There are clear signs of physical harm such as crushing, tearing, or deformation of the package. The item's integrity is compromised, with visible changes up the book, but the cover of the bock fell off, causing the book to be damagea. This damage reflects a critical handling failure that impacts procuct quality and customer satisfaction, as the iter. is no longer in its intended condition.</think fanswer>Nos/answer.
its structure. The robot picked
НAPРКСС Refinement History КИКИИИИ
Round 1:
Detection
Reasoning observe the robot's 's arm or gripper making an unintended + forceful contact with objects in the environment. The impact is clearly visible as the robot's smecth trajectory is disrupted when it strikes either the tote wall, other items, surrounding structures.
Round 2:
Detection No
Reasoning
that the rosot has successfully picked up the item + from the source Location, out then places in what appears to be + the wrong destination. The robot arm moves to a location that doesn't match the expected placement target for this task.
Round (Final Output):
Detection
Reasoning = I see that the robot has successfully picked up the item from the source location, but then places it in what appears to be the wrong destination. The robot arm moves location that doesn't match the expected placement target for this task.
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Reviews:
[{"review_id": "i6md7x3wsQ", "reviewer": "Reviewer_VQku", "summary": "This paper introduces ARMOR which is used for robotic failure detection and reasoning. The authors cast binary success/failure detection and natural-language reasoning as a multi-task self-refinement process. ARMOR iteratively predicts both tasks, conditions on its prior outputs, and refines over several rounds. Training combines offline imitation (expert-conditioned supervision) and online refinement (rollouts with heterogeneous supervision: large-scale sparse binary labels + small-scale dense reasoning labels).\n\nAt inference, multiple refinement trajectories are sampled and the most confident one is selected via an entropy-based self-certainty metric. Across four datasets (RLBench-Fail, Maniskill-Fail, Sparrow-Fail, ARMBench), ARMOR reports state-of-the-art detection and reasoning performance. The work targets the important challenge of scalable, explainable robotic monitoring under limited annotations.", "questions": "1. The paper uses LLM Fuzzy Match and ROUGE-L to measure reasoning quality, but both metrics can be biased or insensitive to factual correctness. Did you conduct any human evaluation, factual consistency analysis, or use alternative metrics (e.g., BLEU, GPT-4-based scoring) to confirm that the improvements reflect genuinely better reasoning rather than surface-level similarity?\n\n2. Since heterogeneous supervision is central to your claim, is it possible to provide evidence that the method’s improvements are not driven by cross-domain artifacts in Table 4 (e.g., using sparse data from one environment and dense data from another)? How robust is ARMOR to changes in this supervision distribution?\n\n3. Since the detection component is a binary classification task, did you consider comparing against smaller vision-only models (e.g., ResNet, ViT, EfficientNet) fine-tuned for failure detection?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. ARMOR reframes robotic failure detection and reasoning as a multi-round, multi-task self-refinement problem. Instead of predicting both tasks jointly in one forward pass, it trains the model to condition on its own past predictions (for both detection and reasoning) and iteratively improve them, which I believe is a interesting approach.\n\n2. The proposed refinement framework is not limited to failure detection. Its iterative conditioning mechanism and multi-task training setup could naturally extend to other vision-language domains such as task planning, human-robot interaction, or embodied reasoning areas where explanation quality and temporal consistency are often more important than raw classification accuracy. This gives the method strong potential impact beyond the specific application studied.", "weaknesses": "1. All reported numbers appear to be single-run result, no mention of multiple seeds, error bars, or statistical significance. Given that inference involves stochastic sampling of trajectories (M=3), performance could vary substantially between runs. Without variance or confidence intervals, large reported gains (up to +100% in reasoning) cannot be verified as statistically meaningful.\n\n2. The paper only compares to generative VLMs (Qwen2.5-VL, Cosmos-Reasoning, LLaVA-NeXT, Claude-3.7). It omits discriminative baselines (like CLIP or SigLIP fine-tuned on detection labels), which are often stronger for classification tasks.\n\n3. The model chooses the best refinement trajectory using entropy (sec 4.3 eq 2) and entropy is used as a proxy for confidence. The paper assumes entropy correlates with correctness, but never tests this hypothesis. There’s no analysis of correlation between low entropy and high accuracy, nor justification for the λ=0.1 choice.\n\n4. Fine-tuning Qwen2.5-VL on 8×H100 GPUs and performing multiple inference rollouts is computationally heavy. The paper does not report inference time, memory usage, or cost, which limits practical value for robotic systems requiring near-real-time feedback.\n\n5. Although not major the ablation study in Table 2 is informative but remains limited in scope. It explores only a few coarse variants of the method and omits key hyperparameter sensitivities that are central to understanding ARMOR’s behavior. In particular, the paper does not analyze how performance varies with the number of refinement rounds (T), the number of sampled trajectories (M), or the masking probability used during offline conditional training. Similarly, the impact of balancing the detection (BCE) and reasoning (NTP) losses is not reported, which makes it difficult to gauge how each objective contributes to the final performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rcpz45s5mZ", "reviewer": "Reviewer_MSS9", "summary": "The paper introduces ARMOR, a VLM trained to detect failures in robotic scenarios. ARMOR introduces a two-task training pipeline separating binary failure detection and reasoning, which are iteratively optimized. A self-refinement objective further enhances reasoning ability. The authors evaluate their approach on existing failure reasoning benchmarks and show that their approach outperforms the selected baselines.", "questions": "- Why not use RL fine-tuning for reasoning refinement?\n- How does Multitask Prediction differ from Offline Imitation?\n- What model size is used, and how does performance scale?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 2, "contribution": 2, "strengths": "- The motivation is clear and the addressed issue is very relevant for robotics.\n- The multi-task training and conditional supervision on type of available ground truth is well justified for the robotics domain.", "weaknesses": "### The clarity and methodology of the proposed method present several concerns\n\n- The second stage of offline imitation seems to just reproduce the textual input. If I understand correctly, the model is provided with the ground truth labels in textual format and then learns to reproduce these outputs. This could possibly lead to the model ignoring the visual modality, making the initial training stage obsolete. \n- The ablation “Multitask Prediction” also indicates this. The model almost performs on par with ARMOR, whereas reasoning is significantly worse. However, the authors don't provide the training details for this baseline, which makes it hard to tell if it's a fair comparison. The difference could be due to shorter training or absence of prompt p_t.\n- It is not clear what supervision signal the model uses during online refinement. Similar methods create a refinement dataset through policy rollouts and ensure each subsequent response is better through a teacher model or reward. It seems like ARMOR solely relies on the supervision from the ground truth target reasoning without any feedback or step-by-step datasets.  This raises the question whether the iterative refinement actually results in better reasoning with multiple iterations.\n- The results provided by the authors regarding performance with multiple model iterations are not convincing. Without any conditioning (step 0), the model reasons poorly, which makes sense, since the model is mainly trained on predicting reasoning conditioned on previous reasonings, and further iterations result in almost no improvements.\n\n### The experimental section is unclear and insufficient to support the main claims\n- Missing comparison with AHA, despite it being evaluated on the same benchmarks\n- Including experiments showcasing the effectiveness of the method under different dense/sparse distributions (e.g., reducing the number of dense annotations further) would support the claim that the method works in regimes where data is annotated with low detail.\n- Experiments showcasing reasoning transferability of reasoning from one domain to another would be more interesting to strengthen the claims of the paper (e.g. Maniskill ( M→R))\n- Motivation and downstream applications (e.g., failure recovery policies, recovery reasoning…) for why correct reasoning even matters in this scenario are missing.\n\n\n### Additional Issues:\n- Usage of the term “two-heads” is misleading. Clarifying that the second “head” is actually just the language model head or the standard VLM would make it easier to grasp the method.\n- Line 240 and subsequent:  Misleading use of “encoder-decoder” terminology for a decoder-only VLM.\n- Missing model sizes\n- No justification/ablation for the CLS token\n- Line 322: Lowest confidence is misleading. Shouldn’t it be the lowest entropy and the highest confidence?\n\nThe paper addresses an important problem and reports promising results, but it remains unclear why the method works. The training pipeline and iterative refinement lack clear intuition and justification, and the experiments do not convincingly support the main claims. I suggest that the authors clarify the method and explain the intuition behind the iterative refinement without actually ensuring that the refinement steps result in incrementally better responses during training.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "GlI0jt9X06", "reviewer": "Reviewer_zLbK", "summary": "The paper addresses failure detection and reasoning in robotic systems, which is indeed an important capability for enhancing the reliability of robotic systems. Prior work either treats this as closed-set classification or relies heavily on fully annotated reasoning labels. The authors propose ARMOR, a VLM that performs multi-round self-refinement to jointly predict binary failure detection and open-ended natural-language reasoning.\n\nARMOR is trained under heterogeneous supervision, combining large-scale sparse binary labels and a small number of dense reasoning annotations. The framework employs two task-specific heads (classification and text generation) trained via a combination of offline imitation and online refinement, allowing it to iteratively improve predictions conditioned on previous outputs. At inference time, multiple refinement trajectories are generated and scored using an entropy-based self-certainty metric, with the most confident prediction selected as output.\n\nExperiments across four domains (RLBench, Maniskill, Sparrow, and ARMBench) show state-of-the-art performance, with up to 30% improvement in detection accuracy and 100% in reasoning quality (LLM fuzzy match) compared to prior baselines such as AHA and SFT variants. Ablations demonstrate that both multi-task design and iterative refinement contribute to performance gains.", "questions": "See the questions above.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The paper addresses an under-explored but crucial problem, robotic failure detection and reasoning. \n\n- The proposed multi-round adaptive refinement allows the model to iteratively improve its predictions, similar to human introspection, and provides more coherent reasoning explanations.\n\n- The authors performed extensive experiments on four diverse robotic datasets (RLBench, ManiSkill, Sparrow, ARMBench), showing clear and consistent gains over strong baselines in both detection accuracy and reasoning quality.\n\n- The authors conduct detailed ablation studies on the refinement rounds, task weighting, and uncertainty metrics, providing convincing evidence of each design component’s impact.", "weaknesses": "- How does the multi-round refinement introduce additional inference cost proportional to the number of rounds? It can be helpful to also include some latency or runtime statistics. In real-time robotic applications, providing timely feedback can be challenging without interrupting task execution. Similarly, the dependency on large pre-trained language models raises deployment challenges for real-time execution. \n\n- For the failure detection of these safety-critical tasks, using these large models with even video inputs can be overkill compared to specialized settings. Since these manipulation tasks are not performed/validated in the wild, why can one not use more overfitting metrics instead of an open-set VLM if one cares more about the binary value of success or not than the detailed reasoning? Including some discussions would further improve the soundness of the paper.\n\n- While the proposed multi-round self-refinement mechanism is well-motivated, the architectural backbone and training strategy primarily build upon the existing vision–language and instruction-tuning framework. The main novelty lies in integrating iterative refinement and multi-task learning, which, though valuable, represents an incremental advance rather than a new paradigm.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ahOGVIZltJ", "reviewer": "Reviewer_2SV2", "summary": "The paper introduce ARMOR, an approach that jointly detects whether a robot execution succeed or failed and then provide explains and building on top of prior work like AHA, and they achieve this through iteratively refines both outputs over several rounds. It is designed for the realistic case where you have many binary success/failure labels but few natural‑language explanations. Training mixes offline imitation (warm‑up + conditioning on ground‑truth previous outputs) with online refinement rollouts; inference runs several refinement trajectories and selects the output with lowest entropy.", "questions": "Refer to the weakness for the questions.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "Strength of the paper:\n-> The method demonstrated to be effiective and efficient, as on RLBench‑Fail & ManiSkill‑Fail (sim), Sparrow‑Fail & ARMBench (real). Detection is measured by Binary Success Rate; reasoning by LLM Fuzzy Match (judge: Claude‑3.7‑Sonnet) and ROUGE‑L; the fuzzy score is computed only on the reasoning text to avoid format bias.\n-> The work also demonstrated that the explicitly target s the common regime with a lot of binary label data, and only a few explains which still able to achieve good performance.\n->Gains hold from simulation to real warehouses and under transfer (R→M, S→A), where naïvely mixing sparse+dense (SFT‑S+D) often collapses reasoning.", "weaknesses": "-> Unreported runtime or inference time latency, as the inference does multiple rounds and samples, so it is good to know the wall-clock or throughput for this, cause it matters for robot deployment.\n\n-> There are many runs on benchmarks from AHA, however there was not any demonstrate of how such VLM model could be used to help with downstream robotics manipulation tasks. \n\n-> How well does this transfer to cross-embodiment to different single arm robot.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)