# Evaluating Uncertainty and Quality of Vision-Language-Action-enabled Robots

arXiv: https://arxiv.org/abs/2507.17049 · HTML: https://mlsanity.com/paper/2507.17049/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Failure recovery and robustness, one of 13 directions in Vision-language-action models.
- Builds on 68 of its 104 references that are in the map.
- First posted on arXiv 22 Jul 2025.

## Placed in

- Vision-language-action models / [Failure recovery and robustness](https://mlsanity.com/vla/failure-recovery-and-robustness/)

## Builds on (most cited first, 30 of 68)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)

## Built on by (most cited first, 17 of 17)

- [LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization](https://mlsanity.com/paper/2510.03827/) (2025)
- [Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms](https://mlsanity.com/paper/2604.23775/) (2026)
- [UAOR: Uncertainty-aware Observation Reinjection for Vision-Language-Action Models](https://mlsanity.com/paper/2602.18020/) (2026)
- [When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering](https://mlsanity.com/paper/2602.22474/) (2026)
- [Metamorphic Testing of Vision-Language Action-Enabled Robots](https://mlsanity.com/paper/2602.22579/) (2026)
- [ReconVLA: An Uncertainty-Guided and Failure-Aware Vision-Language-Action Framework for Robotic Control](https://mlsanity.com/paper/2604.16677/) (2026)
- [An Anatomy of Vision-Language-Action Models: From Modules to Milestones and Challenges](https://mlsanity.com/paper/2512.11362/) (2025)
- [SCALE: Self-uncertainty Conditioned Adaptive Looking and Execution for Vision-Language-Action Models](https://mlsanity.com/paper/2602.04208/) (2026)
- [VISOR: A Vision-Language Model-based Test Oracle for Testing Robots](https://mlsanity.com/paper/2605.10408/) (2026)
- [FATE-VLA:Failue-aware test generation for vision-language-action models](https://mlsanity.com/paper/2606.02307/) (2026)
- [MANGO: Automated Multi-Agent Test Oracle Generation for Vision-Language-Action Models](https://mlsanity.com/paper/2606.24815/) (2026)
- [Assessing Vision-Language Models for Perception in Autonomous Underwater Robotic Software](https://mlsanity.com/paper/2602.10655/) (2026)
- [Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents](https://mlsanity.com/paper/2604.25161/) (2026)
- [VLAMotor: Test-Guided Enhancement of Vision-Language-Action Models via Agent-BasedData Synthesis](https://mlsanity.com/paper/2606.00053/) (2026)
- [Auditing Instruction-Trajectory Mismatches in Multimodal Robot Demonstrations](https://mlsanity.com/paper/2608.07895/) (2026)
- [FabriMAE I Trust Myself? Self-Evaluating VLA Action Generation with Markov Attention Entropy](https://mlsanity.com/paper/2608.16697/) (2026)
- [Outcome-Conditioned End-Effector Geometry Across Vision-Language-Action Policies](https://mlsanity.com/paper/2609.21659/) (2026)

## Abstract (arXiv)

Vision-Language-Action (VLA)-enabled robots integrate visual perception, natural language understanding, and action planning to interpret their environment, comprehend instructions, and perform embodied tasks autonomously. Such robots are typically evaluated through task success rates, i.e., whether a robot performs its intended task, which are commonly used as test oracles for evaluating such robots. Such an evaluation fails to capture the quality of task execution and the robot's confidence in its decisions. In this paper, we adapt eight uncertainty metrics and five quality metrics specifically designed for VLA-enabled robotic manipulation tasks. We assess their effectiveness through a large-scale empirical study involving 908 successful task executions from three state-of-the-art VLA models across four representative robotic manipulation tasks and two robot embodiments. Human domain experts manually labeled task quality, enabling us to analyze the correlation between our proposed metrics and expert judgments, serving as a human oracle for testing such robots. The results reveal that several metrics show moderate to strong correlation with human assessments, highlighting their utility for evaluating task quality and model confidence. Furthermore, we found that some metrics can discriminate between high-, medium-, and low-quality executions from unsuccessful tasks, which is useful when test oracles are absent. Our findings challenge the adequacy of current evaluation practices that rely solely on binary success rates and pave the way for improved real-time monitoring and adaptive enhancement of VLA-enabled robots.