Uncertainty Estimation for VLA Models

VLA: Vision-Language-Action

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5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Latest papers 24

Oct 4, 2026cs.CV

Triggering Generalist Reasoning via Predictive Uncertainty for Dual-System VLA

Dual-system Vision-Language-Action (VLA) models improve real-time robotic control by pairing a slow, reasoning-capable generalist with a fast specialist action expert. However, existing methods invoke the generalist at a fixed frequency, ignoring the fact that decision-making complexity varies throughout a rollout. This static strategy wastes computation in easy phases and can delay renewed reasoning when the scene changes unexpectedly. We propose TUD (Triggering generalist reasoning via predictive Uncertainty for Dual-system VLA), an adaptive inference framework that selectively skips unnecessary generalist calls. TUD measures the cross-step dispersion of action re-predictions at the upcoming chunk slot under the cached generalist context, as a predictive uncertainty signal. This signal captures how much the future action plan shifts as new observations arrive and is computed from forwards the architecture already runs, requiring neither manual phase labels nor an auxiliary uncertainty model. On VLA-Arena, it achieves a higher success rate at matched call budgets than alternative uncertainty baselines while maintaining low wall-clock overhead, and more consistently separates successful from failed rollouts. Also, TUD finds a more favorable cost-success trade-off than non-adaptive baselines, tracing an entire operating curve as a single threshold is varied, and substantially reduces VLM calls at matched success rate. The same trade-off appears in our real-robot experiments, where TUD cuts generalist calls by 75% relative to the strongest fixed-interval baseline while achieving an even higher success rate. Our results suggest that predictive uncertainty provides a practical criterion for adaptive reasoning in efficient VLA control.

Hyemin Yang, Wooseong Jeong, Giwon Lee +1

KAIST

Uncertainty Estimation for VLA Models Efficient VLA Model Inference Test-Time Adaptation of VLA Models VLMs for Robotics Reasoning for VLA Models

Sep 28, 2026cs.RO

Do Not Cut When Uncertain: Rejectable and Calibrated Decision Heads for VLA Policies in Robotic Harvesting

Vision-Language-Action (VLA) policies trained with behavior cloning or flow matching are optimized to output an action trajectory, but they cannot express "I don't know" or "I should not act." In robotic harvesting, occlusion makes single-frame decisions fundamentally ambiguous: identical pixels can correspond either to a cuttable stem or to no stem at all. Existing VLAs are forced to commit, leading to high-confidence errors with irreversible consequences. We argue that the failure mode of a VLA is determined not by backbone scale but by its output interface. We propose Rejectable and Calibrated Decision Heads (RCDH), a typed, rejectable, and calibrated output interface that can be attached to a frozen VLA backbone without retraining or new features. RCDH introduces (i) a decision schema with explicit rejection and ordered, conditional decomposition, and (ii) a calibration procedure for risk-aware abstention. We evaluate RCDH on a robotic harvesting platform with controllable leaf occlusion, comparing generative, enumerated, calibrated, and rejectable interfaces. We show that replacing only the output head restores out-of-distribution usability under occlusion while preserving in-distribution performance. We further test whether the ordering of the rejection space is critical. Our results suggest that the right to refuse, rather than a larger model, is the missing interface for reliable manipulation under uncertainty.

Heng Zhang

Uncertainty Estimation for VLA Models Selective Prediction Agricultural Robotics Robustness of VLA Models Uncertainty Calibration

Sep 21, 2026cs.RO

Capability-Aware Arbitration for Semantic Intent-Based Shared Control

Shared control often allocates robot authority based on confidence in inferred human intent, assuming reliable autonomous execution. When this assumption fails, high intent confidence can cause over-helping. We present a capability-aware shared-control framework in which a vision-language model (VLM) infers human intent and provides semantic-intent confidence, while a vision-language-action (VLA) policy generates autonomous actions. VLA capability confidence is estimated online from the dispersion and local instability of stochastic action trajectories. We design a nonlinear arbitration policy that combines Bayesian-filtered semantic-intent confidence with VLA capability confidence through a sigmoid mapping to adapt robot authority. Our evaluation combined VLM/VLA confidence assessment with a study involving 12 participants performing pick-and-place and bidirectional stacking under in-distribution and out-of-distribution conditions. The proposed method achieved the highest task success rate (92%), compared with manual teleoperation (83%), intent-only arbitration (44%), and fixed equal-weight blending (10%). It also achieved higher control friendliness and lower authority-weighted disagreement than both shared-control baselines. These results demonstrate the benefit of incorporating VLA capability into authority allocation to mitigate over-helping and improve shared-control performance.

Zhaoda Du, Michael Bowman, Xiaoli Zhang

Colorado School of Mines, Golden, CO 80401, USA. · University of Pennsylvania, Philadelphia, PA 19104, USA.

Uncertainty Estimation for VLA Models Human-Robot Collaboration Shared Autonomy VLMs for Robotics

Sep 14, 2026cs.RO

UDAV: Uncertainty-Driven Adaptive VLM Waypoint Planner

Vision-language models (VLMs) can generate routes directly from aerial imagery for off-road navigation, but their predictions provide no indication of reliability. We present UDAV, an Uncertainty-Driven Adaptive VLM Waypoint Planner for UAV-guided UGV navigation. UDAV draws multiple stochastic trajectory predictions, selects their medoid as a self-consistent nominal route, and estimates predictive uncertainty from their spatial dispersion. When the maximum uncertainty across interior waypoints exceeds a threshold, UDAV invokes a reconsideration stage; otherwise, it returns the medoid directly. We evaluate UDAV on 400 held-out trajectory queries from two UAV flights. Stochastic medoid selection reduces the mean average displacement error (ADE) from 147.4 pixels for a deterministic prediction to 115.9 pixels. The complete planner achieves a mean ADE of 110.4 pixels, a 25.1% reduction relative to deterministic planning, while producing valid trajectories for all queries. UDAV also yields the lowest 90th- and 95th-percentile errors among all evaluated configurations, including a higher-budget K=10 consensus baseline. Relative to the K=5 medoid, UDAV reduces these errors from 225.3 and 326.0 pixels to 199.0 and 290.8 pixels, respectively. These results demonstrate that stochastic VLM predictions provide both a stronger nominal route and an actionable uncertainty signal for selectively mitigating large planning errors.

Ghazal Farhani, Shabnam Shabani

The authors are with the Automotive and Surface Transportation Research Centre, National Research Council Canada, London, Ontario, Canada.

Uncertainty Estimation for VLA Models Trajectory Generation Robot Navigation UAV Navigation Vision-Language Navigation Aerial VLN+1

Sep 8, 2026cs.RO

Estimating Semantic Ambiguity via Gaussian Context Distributions for VLM-Driven Traversability Analysis

Autonomous navigation in unstructured environments requires robust scene understanding, yet Vision-Language Models (VLMs) often suffer from semantic ambiguity, where conflicting predictions can lead to dangerous failures. To address this, we present a novel pipeline for vision-based traversability estimation that explicitly models contextual uncertainty. Our approach utilizes Conceptual Anchoring to ground open-vocabulary VLM predictions onto a continuous physical traversability scale. By formulating the model's responses as a Gaussian Context Distribution (GCD), we derive both a dense traversability map and a dense uncertainty map based on the statistical properties of the distribution. Experimental validation on the real-world GOOSE dataset demonstrates that our proposed uncertainty metric effectively correlates with sources of ambiguity, such as visual artifacts and mixed terrain overlap. The method exhibits competitive performance while offering the distinct advantage of providing statistical uncertainty estimates to address semantic ambiguity, enabling safer and more reliable autonomous behavior in complex outdoor settings.

Ramona Häuselmann, Mario A. V. Saucedo, Christoforos Kanellakis +1

Robotics & AI Team, Department of Computer, Electrical and Space Engineering, Luleå University of Technology, Luleå SE-97187, Sweden.

Uncertainty Estimation for VLA Models Embodied Navigation VLMs for Robotics

Aug 26, 2026cs.RO

LM-X: Explainable Vision--Language--Action Modeling via Progress, Event, and Uncertainty Prediction

Large-scale vision--language--action (VLA) policies have advanced generalist robot control, yet most remain stimulus-to-action black boxes: actions are exposed, but their explanatory state is not. They provide no native account of three explanatory signals: task progress, the next semantic transition, or local command reliability. Prior work shows that progress and event structure aid long-horizon control and that uncertainty supports monitoring; however, such capabilities are typically added or extracted only after action pretraining. The field therefore lacks a VLA foundation model whose explanatory state is jointly pretrained with control. Drawing on biological sensorimotor organization, in which outcome-sensitive, event-segmented, and probabilistic predictions structure behavior, we introduce LM-X. LM-X learns three directly supervised online signals: return-to-go (RTG) estimates visible progress and state quality; event-to-go (ETG) predicts the action sequence to the next semantic event; and heteroscedastic action-flow variance reports local command reliability. RTG conditions ETG and both condition action generation; uncertainty is estimated inside the action expert, making explanation part of control rather than a post-hoc description. We pretrain LM-X on more than 20,000 hours of heterogeneous real-robot trajectories, including over 1,000 hours of failed rollouts. LM-X achieves 74.1% success on 50 randomized-hard RoboTwin2.0 tasks and 73.5% on seven real-robot tasks, compared with 55.4% and 50.7% for GR00T N1.7. Its signals track progress and regression, anticipate event-scale motion, detect high-error actions, and provide advance failure warning. These results establish LM-X as an explainable VLA foundation model that couples transparent predictive state with stronger generalist control. Github: https://github.com/loongOpen/LoongWu-LM-X-VLA

Jin Lou, Zhiyuan Jing, Xupeng Wang +21

Humanoid Robot (Shanghai) Co., Ltd. · E-surfing Digital Life Technology Co., Ltd., China Telecom

Robotic Control Uncertainty Estimation for VLA Models Efficient VLA Models Vision-Language-Action Models

Aug 17, 2026cs.AI

MAE I Trust Myself? Self-Evaluating VLA Action Generation with Markov Attention Entropy

Vision-Language-Action models (VLAs) integrate visual perception, language instruction, and action generation into end-to-end policies across heterogeneous architectures. However, enabling VLAs to self-evaluate their action generation reliability without external supervision remains a major challenge. Existing methods either rely on expert annotations or estimate uncertainty only from output statistics, largely ignoring internal signals. In this work, we observe that internal visual modality entropy exhibits consistent distinctions between successful and failed tasks across heterogeneous VLAs. Although VLAs' architectures differ in their action generation, we show that they share a common latent action generation abstraction evolving under visual perception, language instruction, and State Input, which we formulate as a Conditional Generative Markov Chain. Based on this formulation, we propose MAE (Markov Attention Entropy), a self-evaluation framework that directly converts internal attention signals into architecture-aware reliability scores, and introduce LIBERO-Reflect, a 4,000-episode benchmark combining 2,000 standard episodes and 2,000 challenging episodes across four subsets. Extensive experiments across heterogeneous VLA architectures and diverse scenarios show that MAE consistently outperforms state-of-the-art baselines on AUPR, AUROC, and FPR@95.

Aniri, Chen Yilin, Jinhe Bi +7

Ludwig Maximilian University of Munich · Munich Center for Machine Learning · East China University of Science and Technology +2

Uncertainty Estimation for VLA Models LLM Uncertainty Estimation Safety Evaluation of VLA Models

Aug 5, 2026cs.RO

GUARD: Grounding Uncertainty and Ablation-Based Risk Detection for Diffusion-Based VLAs

Diffusion-based vision-language-action (VLA) policies can generate plausible actions even when their predictions are weakly grounded in the visual and language evidence defining the task. We introduce GUARD, a test-time failure detection method that measures this grounding without modifying the pretrained policy. GUARD estimates the influence of token-indexed entries in the final vision-language model key-value (KV) cache, constructs counterfactual caches by ablating salient KV entries, and compares their denoising responses with the original conditioning. Based on the comparison, we derive GUARD diagnostic stream including sensitivity, attention entropy, modality bias, and grounding efficiency, which are calibrated online and processed by a lightweight temporal classifier. We evaluate GUARD under task-held-out splits across five policy-benchmark settings, using Pi0, SmolVLA, and Alpamayo-1.5 on LIBERO, SimplerEnv, MetaWorld, and PhysicalAI-AV. GUARD achieves the best ROC-AUC on four of five unseen-task settings and ranks second on the remaining setting, improving the average unseen-task ROC-AUC by 5.73 percentage points over the strongest competing runtime monitor while remaining within 0.19 points of the best seen-task average. These results show that directly probing action-head dependence on multimodal evidence provides a transferable failure signal across policies, tasks, embodiments, and domains.

Suhas Hegde, Jitendra Yasaswi Bharadwaj Katta

Bosch Research India

Uncertainty Estimation for VLA Models Vision-Language Grounding Vision-Language-Action Models

Jul 30, 2026cs.AI

The Geometry of Flow-Matching Uncertainty: A Cost-free Uncertainty Proxy and Its Application in Flow-based VLA Failure Detection

Flow matching (FM) has become a popular action head paradigm for modern embodied models. However, as a conditional generative model, it does not explicitly expose its inherent uncertainty, producing faulty action chunks even when it misinterprets the scene or encounters out-of-distribution (OOD) inputs. Therefore, determining when an FM-generated action can be trusted is essential for safe deployment, yet existing uncertainty estimation methods on real-time control suffer from several issues: extra training budget, high computational overhead, and low generalization ability. In this work, we provide a geometric interpretation of FM uncertainty in the velocity field, showing that uncertainty manifests as deviation from an ideal affine-isotropic contraction field. Building on this observation, we introduce denoising acceleration (

\mathrm{accel}

), a highly-generalizable and cost-free uncertainty proxy that measures the bending of the denoising trajectory from a single forward pass, without additional model evaluations, training, or resampling. We theoretically and empirically demonstrate that

\mathrm{accel}

is a faithful proxy for FM uncertainty and further test its utility in online failure detection. Results show that

\mathrm{accel}

identifies failing rollouts well before termination, matching or even outperforming costly resampling- and training-based baselines across settings under realistic deployment budget. Code and demos available at: https://github.com/rrrrrrzy/fm-geometry.

Ziyang Rao, Yiren Zhao, Weiyu Guo +3

1HKUST(GZ) 2HKUST 3CUHK 4AI² Roboticss

Flow Matching Uncertainty Estimation for VLA Models Flow-Based Generative Modeling

Jul 11, 2026cs.RO

SUREFlow: State-space Uncertainty-aware REsidual Flow Matching for Robust Robot Manipulation

Generative vision-language-action policies have advanced robot manipulation, but they often exhibit instability under noise, partial observability, and stochastic initial conditions. During extended rollouts, small velocity errors accumulate, degrading execution reliability. Existing diffusion and flow-based policies typically assume homoscedastic residuals and lack explicit uncertainty modeling within action generation, limiting robustness during iterative rollout. We propose SUREFlow, a state-space uncertainty-aware residual flow matching framework built on a Mamba backbone. The method jointly predicts action velocities and input-dependent residual uncertainty, enabling selective refinement of unreliable action dimensions without environment feedback while preserving computational efficiency. On LIBERO, SUREFlow achieves 92.5% average success rate (SR), outperforming the Mamba-based MaIL by 34.2%. On LIBERO-PRO, it attains around 49% SR using only 179M parameters, achieving performance comparable to large VLAs with 3-7B parameters. SUREFlow source code is available on: https://github.com/tanvirnwu/SUREFlow

Md Tanvir Islam, Sai Navaneet Peddapalli, Sangmoon Lee +1

School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea

Flow Matching Uncertainty Estimation for VLA Models Language-Conditioned Robot Manipulation Robustness of VLA Models

Jul 9, 2026cs.LG

When Thinking Hurts: Epistemic Signals in the Reasoning Chains of Visual Language Models

Uncertainty quantification for visual language models (VLMs) conventionally targets the answer token distribution. We provide the first three-family empirical characterisation of answer entropy behaviour in thinking-mode VLMs. Running four models on identical POPE adversarial samples, we find three qualitatively distinct patterns: Qwen3-VL-8B-Thinking shows complete collapse (ans H AUROC = 0.492); GLM-4.1V-9B-Thinking shows no collapse (0.716); and InternVL3-8B shows selective thinking (chains on only 50% of samples, ans H = 0.675 full / 0.602 thinking-only). Across all three thinking-mode models, thinking chain entropy outperforms answer entropy on the subset where chains are generated (0.647, 0.759, 0.608 vs. 0.492, 0.716, 0.602 respectively), suggesting chain signals are the more reliable predictor whenever chains are present. This holds strongly for Qwen and GLM, but with only marginal and statistically unreliable advantage for InternVL3 (n_FP = 17). A 300-sample VQAv2 pilot confirms chain entropy (0.680) outperforms answer entropy (0.595) on VQAv2 questions, with the gap largest for free-form answers (0.733 vs. 0.467). On harder reasoning tasks (HallusionBench) both Qwen models show moderate signal (approx. 0.64), consistent with incomplete pre-commitment on difficult questions. We additionally document structured abstention affecting 12-22% of queries with asymmetry toward absent-object queries, and a practical abstention gate raising accuracy from 71.0% to 93.8% at 62.7% coverage with no additional inference cost.

Mayank Singal

Independent Researcher.

VLM Evaluation Uncertainty Estimation for VLA Models Vision-Language Models VLM Reasoning

Jun 30, 2026cs.CV

Visual Semantic Entropy: Do Vision Language Models Recognize Visual Ambiguity?

Vision-language models can produce confident answers on visually ambiguous inputs, resulting in biased predictions. Common entropy-based methods, such as Semantic Entropy (SE), rely on output diversity. Yet our analysis shows that overconfident visual embeddings suppress output diversity under stochastic decoding, causing SE to underestimate uncertainty in such cases. Recent methods instead probe output diversity through input perturbations, including textual paraphrasing or joint text-image perturbations, and show improved performance. We study these approaches and reveals that the resulting variability is often dominated by textual changes rather than visual evidence, causing uncertainty estimates to reflect prompt sensitivity rather than visual ambiguity. We therefore propose Visual Semantic Entropy (VSE), which perturbs only the image to probe nearby visual variations while keeping the text query fixed. VSE measures uncertainty by clustering generated answers into semantic prototypes and computing the mass-weighted dispersion among them. Extensive evaluation across five modern vision-language models and five diverse VQA benchmarks demonstrates that VSE effectively captures visual ambiguity, establishing a new state-of-the-art for VLM uncertainty estimation.

Ta Duc Huy, Trang Nguyen, Townim Chowdhury +5

Australian Institute for Machine Learning, Adelaide University, Australia · Flinders University, Australia

VLM Evaluation Visual Question Answering Uncertainty Estimation for VLA Models Uncertainty Quantification

Jun 18, 2026cs.RO

GroundControl: Anticipating Navigation Failures in Vision-Language Agents via Trajectory-Consistent Uncertainty Estimates

Vision-language navigation agents achieve competitive average success on benchmark tasks, yet failures often arise through predictable trajectory-level breakdowns such as oscillation, stagnation, or inefficient detours. Reliable deployment, therefore, requires uncertainty signals that anticipate emerging failure dynamics during execution rather than reflect only instantaneous action entropy. We introduce \emph{GroundControl}, a trajectory-consistent uncertainty estimator defined as statistical deviation from nominal goal-directed distance-to-goal dynamics aggregated over an episode. GroundControl models distance evolution using a constant-velocity Kalman filter and combines normalized innovation statistics with complementary trajectory features capturing progress, monotonicity, path efficiency, and oscillatory behavior. The resulting uncertainty score reflects geometric and temporal inconsistency in navigation behavior rather than local prediction dispersion. To evaluate uncertainty quality independently of task success, we formalize \emph{Selective Risk--Coverage Navigation (SRCN)}, a protocol that measures how effectively an uncertainty score ranks episodes by failure or inefficiency using risk--coverage curves and AURC / E-AURC summaries. Across five EB-Navigation splits (

N=300

episodes), trajectory-consistent uncertainty achieves near-oracle ordering under success-based selective risk, with weighted-average

\mathrm{E\text{-}AURC}_{\mathrm{SR}}=0.0024

for the GPT-4o model, substantially outperforming entropy-, conformal-, and heuristic baselines. Under SPL-based selective evaluation, GroundControl consistently achieves the lowest AURC and E-AURC across models and navigation splits. These results show that modeling deviation from goal-directed dynamics provides an interpretable and robust signal for anticipating navigation failures in vision-language agents.

Nastaran Darabi, Divake Kumar, Sina Tayebati +2

University of Illinois at Chicago (UIC)

Uncertainty Estimation for VLA Models Selective Prediction Vision-Language Navigation Agent Trajectory Analysis

Jun 18, 2026cs.RO

Perturbation-Based Epistemic Uncertainty for Failure Detection in Vision-Language-Action Models

Vision-Language-Action (VLA) models have shown strong performance in robotic manipulation, but reliable uncertainty quantification remains challenging, particularly under distribution shift. Unlike autoregressive policies, many modern VLA models generate continuous actions through regression or flow-based generation, where explicit predictive probabilities are unavailable. Moreover, stochastic action sampling primarily captures action-generation variability under a fixed model, while failure detection under distribution shift can benefit from capturing uncertainty in the model itself. Motivated by Bayesian perspectives on local model variations, we propose perturbation-based failure detection (PFD), a training-free framework for estimating epistemic uncertainty in VLA models through low-rank weight perturbations. Specifically, we inject random low-rank perturbations into selected transformer weight matrices and estimate epistemic uncertainty from disagreement across perturbed action predictions. Experiments on LIBERO-PRO show that PFD achieves the highest average AUROC and balanced accuracy among the evaluated methods while consistently outperforming stochastic action sampling across distribution shifts. Real-world robot experiments further demonstrate that PFD provides a competitive failure-detection signal under an unseen object shift.

Yousung Lee, Dongsoo Har

Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.

Uncertainty Estimation for VLA Models Robotic Manipulation Vision-Language-Action Models Epistemic Uncertainty

Jun 16, 2026cs.RO

Uncertainty Quantification for Flow-Based Vision-Language-Action Models

Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets. Despite their strong empirical performance in robotic manipulation, VLAs lack mechanisms to quantify confidence in their predictions and to detect when their actions may be unreliable. This presents a critical limitation for real-world deployment in non-stationary environments, where models inevitably encounter scenarios outside their pretraining distribution and may fail without warning. To address this, we derive an efficient method for quantifying epistemic uncertainty in flow-matching models by leveraging velocity-field disagreement (VFD) across a small ensemble. We successfully use this uncertainty estimate for failure detection during deployment and active fine-tuning of flow-based VLAs. To this end, we propose SAVE, a framework for uncertainty-guided active multitask fine-tuning that reduces the number of costly expert demonstrations required to adapt VLAs to new tasks. Through extensive experiments on the LIBERO benchmark, we demonstrate that VFD yields better-calibrated uncertainty estimates predictive of downstream performance, that VFD achieves strong performance in detecting failures, and that uncertainty-guided data acquisition with SAVE requires at least 22% fewer samples than baselines. In summary, our work shows that quantifying epistemic uncertainty in flow-based VLAs improves both failure awareness and adaptation. Project website: tum-lsy.github.io/uq_vla/.

Ralf Römer, Maximilian Seeliger, Saida Liu +5

TU Munich · ETH Zurich · TU of Munich +1

Flow Matching Uncertainty Estimation for VLA Models Robot Failure Detection VLM Adaptation Vision-Language-Action Models

Jun 1, 2026cs.CV

FUSE: Quantifying Uncertainty in Vision-Language Models by Bayesian Fusing Epistemic and Aleatoric Uncertainty

Vision-language models (VLMs) are playing an increasingly important role across multiple domains. In many applications, such as robotics, it is crucial to quantify the uncertainty in the output of these models. } We develop FUSE, a probabilistic framework for capturing two complementary sources of uncertainty in vision-language modeling: (i) aleatoric embedding-level uncertainty derived from input data vision-language ambiguity, and (ii) epistemic model-level uncertainty estimated from the semantic response diversity of VLMs. Our approach formulates a Bayesian fusion mechanism that analytically combines these uncertainty sources to produce a scalar measure of uncertainty. This measure can be used to reliably predict the model's output correctness for downstream applications. We demonstrate that our method outperforms baselines and achieves SOTA uncertainty calibration.

Harry Zhang, Luca Carlone

Massachusetts Institute of Technology

Uncertainty Estimation for VLA Models Vision-Language Models Uncertainty Quantification VLM Calibration Uncertainty Calibration

May 28, 2026cs.CV

Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?

Spatial reasoning benchmarks typically evaluate whether vision-language models can derive the correct answer from a visual observation. Yet in real 3D environments, the observation itself may be unreliable: occlusion can remove task-relevant evidence, while perspective can make visible geometry misleading. Reliable spatial reasoning therefore requires more than answering a question correctly. A model must also assess whether its current observation provides sufficient and trustworthy evidence for that answer. We introduce SPATIALUNCERTAIN, a controlled evaluation framework for studying viewpoint-dependent observational uncertainty. We study two complementary failure modes: missing evidence caused by occlusion and misleading evidence caused by perspective. We further evaluate whether models can recognize when the current view is unreliable and identify a more informative observation. Across eight open- and closed-source vision-language models, we find that model behavior does not track the reliability of visual evidence. Models do not reliably become more cautious as evidence disappears, and under perspective conflict, their judgments increasingly follow projected appearance rather than the unchanged physical 3D relation. Internal analysis suggests a corresponding representational asymmetry: projected 2D relations are readily available, whereas the underlying physical 3D relation is barely decodable. Moreover, models that can identify an informative viewpoint when explicitly asked often fail to recognize when such an additional view is needed. These failures are not fully resolved by prompting or fine-tuning, and providing a better viewpoint is substantially more effective than adding depth information to the same misleading observation. Our results identify assessing the reliability of visual observations as a distinct and missing component of current spatial reasoning evaluation.

Yue Zhang, Zun Wang, Han Lin +3

UNC Chapel Hill · Google Research

VLM Evaluation Spatial Reasoning Benchmarks Uncertainty Estimation for VLA Models Vision-Language Models 3D Spatial Reasoning Visually Grounded Reasoning+1

May 28, 2026cs.RO

VLAConf: Calibrated Task-Success Confidence for Vision-Language-Action Models

Confidence estimation for Vision-Language-Action (VLA) models is essential for robots to perform manipulation tasks in the open world, providing crucial signals for risk-sensitive decision-making and failure anticipation. Existing confidence estimation methods typically rely on ensemble-based paradigms or action-token probabilities to predict the likelihood of task success. However, they still encounter challenges in computational efficiency and cross-architecture generalizability. These methods usually require repeated sampling, leading to inference inefficiency, and are restricted to VLA models with discrete action outputs, making them difficult to apply to continuous action spaces. To address this issue, we propose VLAConf, a one-class discriminative confidence framework. By leveraging frozen pretrained VLA internal representations, VLAConf directly estimates step-wise anomaly scores in a single forward pass using a lightweight confidence head, thereby eliminating the overhead of exhaustive resampling. We additionally use step-conditioned modeling to encode rollout-phase information along the manipulation trajectory. Experiments on the LIBERO benchmark demonstrate that VLAConf significantly improves the quality of the confidence signal constructed for post-hoc calibration, outperforming existing baselines by a large margin in inference efficiency. The effectiveness of VLAConf is further validated in real-robot experiments. To access the source code and supplementary videos, visit https://sites.google.com/view/vlaconf.

Dehao Huang, Aoxiang Gu, Chengjie Zhang +5

Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China. · Zhongguancun Academy, Beijing, China · National Cybersecurity Academy, Wuhan University, Wuhan, China.

Uncertainty Estimation for VLA Models Probability Calibration Robotic Manipulation Vision-Language-Action Models One-Class Classification

May 26, 2026cs.CV

Uncertainty-Aware Gaussian Map for Vision-Language Navigation

Vision-Language Navigation (VLN) requires an agent to navigate 3D environments following natural language instructions. During navigation, existing agents commonly encounter perceptual uncertainty, such as insufficient evidence for reliable grounding or ambiguity in interpreting spatial cues, yet they typically ignore such information when predicting actions. In this work, we explicitly model three forms of perceptual uncertainty (i.e., geometric, semantic, and appearance uncertainty) and integrate them into the agent's observation space to enable informed decision-making. Concretely, our agent first constructs a Semantic Gaussian Map (SGM), composed of differentiable 3D Gaussian primitives initialized from panoramic observations, that encodes both the geometric structure and semantic content of the environment. On top of SGM, geometric uncertainty is estimated through variational perturbations of Gaussian position and scale to assess structural reliability; semantic uncertainty is captured by perturbing Gaussian semantic attributes to reveal ambiguous interpretations; and appearance uncertainty is characterized by Fisher Information, which measures the sensitivity of rendered observations to Gaussian-level variations. These uncertainties are incorporated into SGM, extending it into a unified 3D Value Map, which grounds them as affordances and constraints that support reliable navigation. Comprehensive evaluations across multiple VLN benchmarks show the effectiveness of our agent.

Jianzhe Gao, Rui Liu, Yuxuan Xu +6

The State Key Lab of Brain-Machine Intelligence, Zhejiang University · Department of Foundation model, 2012 Labs, Huawei · Noah’s Ark Lab, 2012 Labs, Huawei +1

Uncertainty Estimation for VLA Models Vision-Language Navigation 3D Scene Understanding

May 20, 2026cs.CV

Look-Closer-Then-Diagnose: Confidence-Aware Ultrasound VQA via Active Zooming

Vision-Language Models (VLMs) have significantly advanced medical visual question answering, yet their performance in ultrasound remains suboptimal. In clinical practice, sonographers explicitly focus on lesion regions to formulate reports, though diagnostic interpretations sometimes vary due to inherent subjectivity. However, existing VLMs are not explicitly structured to interactively zoom into lesions prior to diagnosis; moreover, they typically treat annotations as unbiased ground truths, failing to account for their inherent subjectivity and ambiguity. In this paper, we propose a framework specifically designed to consider the sonographer's cognitive workflow. We first introduce a structured Zoom-then-Diagnose paradigm, which replicates the interactive search process to enable lesion-focused reasoning. Furthermore, within the Group Relative Policy Optimization (GRPO) framework, we introduce an uncertainty-aware reward derived from stochastic group-wise rollouts to estimate prediction consistency as a proxy for model confidence. Together, these two components encourage the model to reinforce accurate predictions on clear cases while remaining cautious under ambiguity. Experiments across liver, breast, and thyroid datasets show that our framework improves lesion localization by 39.3%, demonstrating that our model has learned the ability to actively look closer and diagnose.

Yue Zhou, Erxuan Wu, Yikang Sun +5

Computer Aided Medical Procedures (CAMP), TU Munich, Germany · Munich Center for Machine Learning (MCML), Munich, Germany · Zhongshan Hospital, Fudan University, China +1

Visual Question Answering Uncertainty Estimation for VLA Models Medical VQA Ultrasound Imaging Medical Image Localization RL for VLMs

May 11, 2026cs.SE

VISOR: A Vision-Language Model-based Test Oracle for Testing Robots

Testing robots requires assessing whether they perform their intended tasks correctly, dependably, and with high quality, a challenge known as the test oracle problem in software testing. Traditionally, this assessment relies on task-specific symbolic oracles for task correctness and on human manual evaluation of robot behavior, which is time-consuming, subjective, and error-prone. To address this, we propose VISOR, a Vision-Language Model (VLM)-based approach for automated test oracle assessment that eliminates the need of expensive human evaluations. VISOR performs automated evaluation of task correctness and quality, addressing the limitations of existing symbolic test oracles, which are task-specific and provide pass/fail judgments without explicitly quantifying task quality. Given the inherent uncertainty in VLMs, VISOR also explicitly quantifies its own uncertainty during test assessments. We evaluated VISOR using two VLMs, i.e., GPT and Gemini, across four robotic tasks on over 1,000 videos. Results show that Gemini achieves higher recall while GPT achieves higher precision. However, both models show low correlation between uncertainty and correctness, which prevents using uncertainty as a correctness predictor.

Prasun Saurabh, Pablo Valle, Aitor Arrieta +2

Simula Research Laboratory and Oslo Metropolitan University · Mondragon University · National Institute of Informatics

Uncertainty Estimation for VLA Models Automated Software Testing Automated Evaluation VLMs for Robotics

Apr 22, 2026cs.RO

Temporal Difference Calibration in Sequential Tasks: Application to Vision-Language-Action Models

Recent advances in vision-language-action (VLA) models for robotics have highlighted the importance of reliable uncertainty quantification in sequential tasks. However, assessing and improving calibration in such settings remains mostly unexplored, especially when only partial trajectories are observed. In this work, we formulate sequential calibration for episodic tasks, where task-success confidence is produced along an episode, while success is determined at the end of it. We introduce a sequential extension of the Brier score and show that, for binary outcomes, its risk minimizer coincides with the VLA policy's value function. This connection bridges uncertainty calibration and reinforcement learning, enabling the use of temporal-difference (TD) value estimation as a principled calibration mechanism over time. We empirically show that TD calibration improves performance relative to the state-of-the-art on simulated and real-robot data. Interestingly, we show that when calibrated using TD, the VLA's single-step action probabilities can yield competitive uncertainty estimates, in contrast to recent findings that employed different calibration techniques.

Shelly Francis-Meretzki, Mirco Mutti, Yaniv Romano +1

1Technion - Israel Institute of Technology.

Uncertainty Estimation for VLA Models Vision-Language-Action Models VLMs for Robotics Uncertainty Calibration

Apr 17, 2026cs.RO

ReconVLA: An Uncertainty-Guided and Failure-Aware Vision-Language-Action Framework for Robotic Control

Vision-language-action (VLA) models have emerged as generalist robotic controllers capable of mapping visual observations and natural language instructions to continuous action sequences. However, VLAs provide no calibrated measure of confidence in their action predictions, thus limiting their reliability in real-world settings where uncertainty and failures must be anticipated. To address this problem we introduce ReconVLA, a reliable conformal model that produces uncertainty-guided and failure-aware control signals. Concretely, our approach applies conformal prediction directly to the action token outputs of pretrained VLA policies, yielding calibrated uncertainty estimates that correlate with execution quality and task success. Furthermore, we extend conformal prediction to the robot state space to detect outliers or unsafe states before failures occur, providing a simple yet effective failure detection mechanism that complements the action-level uncertainty. We evaluate ReconVLA in both simulation and real robot experiments across diverse manipulation tasks. Our results show that conformalized action predictions consistently improve failure anticipation, reduce catastrophic errors, and provide a calibrated measure of confidence without retraining or modifying the underlying VLA.

Lingling Chen, Zongyao Lyu, William J. Beksi

Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, USA

Uncertainty Estimation for VLA Models Robot Failure Detection Uncertainty Quantification Conformal Prediction Vision-Language-Action Models Vision-Based Robot Control

Dec 1, 2025cs.RO

Transport Discrepancy as a Reliability Signal for Vision-Language-Action Models

Vision-language-action (VLA) models that generate continuous action chunks via flow matching lack an internal signal for judging whether a given prediction is reliable. Distribution shift and long-horizon rollouts can push backbone representations away from the region the action head decodes reliably, yet the policy has no mechanism to detect or react to this drift. We observe that the cost of transporting observation features to the action representation in a shared feature space rises precisely when such drift occurs, providing a per-step reliability estimate without extra supervision. Building on this observation, we propose DiG (Discrepancy Gate), a lightweight plug-in module for flow-matching VLA policies. DiG computes a sliced Wasserstein transport cost between backbone features and the action expert's own input projection, maps it through an exponential gate, and uses the gate to modulate both a residual feature refinement and the training loss. At inference time, the gate enables DiG-Refinefine, an iterative refinement process that corrects action chunks before execution. Experiments on both simulation and real-world scenarios show that DiG consistently improves success rates, with the largest gains under distribution shift and on long-horizon tasks.

Wanpeng Zhang, Ye Wang, Hao Luo +6

1Peking University · 3BeingBeyond · 2Renmin University of China

Uncertainty Estimation for VLA Models Vision-Language-Action Models Robustness of VLA Models