SAFE: Multitask Failure Detection for Vision-Language-Action Models

• URL: http://arxiv.org/abs/2506.09937v2
• Date: Thu, 30 Oct 2025 07:02:35 GMT
• Title: SAFE: Multitask Failure Detection for Vision-Language-Action Models
• Abstract summary: Vision-language-action models (VLAs) have shown promising robotic behaviors across a diverse set of manipulation tasks.<n>They achieve limited success rates when deployed on novel tasks out of the box.<n>We propose SAFE, a failure detector for generalist robot policies such asVLAs.
• Score: 28.746068068444924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While vision-language-action models (VLAs) have shown promising robotic behaviors across a diverse set of manipulation tasks, they achieve limited success rates when deployed on novel tasks out of the box. To allow these policies to safely interact with their environments, we need a failure detector that gives a timely alert such that the robot can stop, backtrack, or ask for help. However, existing failure detectors are trained and tested only on one or a few specific tasks, while generalist VLAs require the detector to generalize and detect failures also in unseen tasks and novel environments. In this paper, we introduce the multitask failure detection problem and propose SAFE, a failure detector for generalist robot policies such as VLAs. We analyze the VLA feature space and find that VLAs have sufficient high-level knowledge about task success and failure, which is generic across different tasks. Based on this insight, we design SAFE to learn from VLA internal features and predict a single scalar indicating the likelihood of task failure. SAFE is trained on both successful and failed rollouts and is evaluated on unseen tasks. SAFE is compatible with different policy architectures. We test it on OpenVLA, $\pi_0$, and $\pi_0$-FAST in both simulated and real-world environments extensively. We compare SAFE with diverse baselines and show that SAFE achieves state-of-the-art failure detection performance and the best trade-off between accuracy and detection time using conformal prediction. More qualitative results and code can be found at the project webpage: https://vla-safe.github.io/

Related papers

• TrapVLA: Trapping Vision-Language-Action Models in Configured Failure Modes [65.10128145059127]
  This work introduces Configured Failure Trapping, a novel backdoor attack task against Vision-Language-Action (VLA) models.<n>We propose an effective data engine for synthesizing high-quality target trajectories and an automated suite for measuring configured-failure fidelity.<n>Experiments show that TrapVLA effectively injects configured failure modes into VLA models while largely preserving performance on clean data.
  arXiv  Detail & Related papers  (2026-08-27T03:44:49Z)
• Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents [58.49879338545782]
  Long-horizon tasks are common in real-world robotic deployments, yet failure detection for such tasks remains underexplored.<n>We present Foresight, a failure detection framework that monitors manipulation trajectories using latent representations from an action-conditioned world model.<n>Our results suggest that action-conditioned world-model embeddings provide a scalable representation for reliable failure monitoring in long-horizon manipulation.
  arXiv  Detail & Related papers  (2026-06-22T09:32:28Z)
• VLA-FAIL: Efficient Task Failure Detection for Finetuned Vision-Language-Action Models [19.137291311347788]
  Vision-language--action models (VLAs) achieve state-of-the-art performance on many robotic tasks, yet they can still behave unpredictably in out-of-distribution scenarios.<n>We propose VLA-FAIL, a framework that combines two novel failure detectors with minimal overhead, without requiring failure data.
  arXiv  Detail & Related papers  (2026-06-19T12:51:21Z)
• Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring [35.34120853605602]
  textbf-and-Seek is a framework that formulates VLA failure detection as a coarsely supervised learning problem.<n>We evaluate Hide-and-Seek on LIBERO, VLABench, and a real-world robotic platform across three representative VLA policies.
  arXiv  Detail & Related papers  (2026-05-29T04:40:12Z)
• Uncovering Linguistic Fragility in Vision-Language-Action Models via Diversity-Aware Red Teaming [64.48633529149579]
  We propose a novel framework to expose the vulnerabilities of Vision-Language-Action (VLA) models against linguistic variations.<n>Our method reduces the average task success rate from 93.33% to 5.85%, demonstrating a scalable approach to stress-testing VLA agents.
  arXiv  Detail & Related papers  (2026-04-07T08:43:36Z)
• Detect Repair Verify for Securing LLM Generated Code: A Multi-Language Empirical Study [10.18490328199727]
  Security is often addressed through a Detect--Repair--Verify (DRV) loop that detects issues, applies fixes, and verifies the result.<n>This work studies such a workflow for project-level artifacts and addresses four gaps: L1, the lack of project-level benchmarks with executable function and security tests; L2, limited evidence on pipeline-level effectiveness beyond studying detection or repair alone; L3, unclear reliability of detection reports as repair guidance; and L4, uncertain repair trustworthiness and side effects under verification.
  arXiv  Detail & Related papers  (2026-03-01T03:41:24Z)
• CycleVLA: Proactive Self-Correcting Vision-Language-Action Models via Subtask Backtracking and Minimum Bayes Risk Decoding [60.06899554269808]
  CycleVLA is a system that equips Vision-Language-Action models (VLAs) with proactive self-correction.<n>CycleVLA achieves this by integrating a progress-aware VLA that flags critical subtask transition points.<n>Extensive experiments show that CycleVLA improves performance for both well-trained and under-trained VLAs.
  arXiv  Detail & Related papers  (2026-01-05T17:31:01Z)
• Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models [53.20969621498248]
  We propose an automatic robot failure synthesis approach that procedurally perturbs successful trajectories to generate diverse planning and execution failures.<n>We construct three new failure detection benchmarks: RLBench-Fail, BridgeDataV2-Fail, and UR5-Fail.<n>We then train Guardian, a VLM with multi-view images for detailed failure reasoning and detection.
  arXiv  Detail & Related papers  (2025-12-01T17:57:27Z)
• AttackVLA: Benchmarking Adversarial and Backdoor Attacks on Vision-Language-Action Models [60.39655329875822]
  Vision-Language-Action (VLA) models enable robots to interpret natural-language instructions and perform diverse tasks.<n>Despite growing interest in attacking such models, the effectiveness of existing techniques remains unclear.<n>We propose AttackVLA, a unified framework that aligns with the VLA development lifecycle.
  arXiv  Detail & Related papers  (2025-11-15T10:30:46Z)
• Learning Affordances at Inference-Time for Vision-Language-Action Models [50.93181349331096]
  In robotics, Vision-Language-Action models (VLAs) offer a promising path towards solving complex control tasks.<n>We introduce Learning from Inference-Time Execution (LITEN), which connects a VLA low-level policy to a high-level VLM that conditions on past experiences.<n>Our approach iterates between a reasoning phase that generates and executes plans for the low-level VLA, and an assessment phase that reflects on the resulting execution.
  arXiv  Detail & Related papers  (2025-10-22T16:43:29Z)
• Failure Prediction at Runtime for Generative Robot Policies [6.375597233389154]
  Early failure prediction during runtime is essential for deploying robots in human-centered and safety-critical environments.<n>We propose FIPER, a framework for failure prediction for generative robot policies that does not require failure data.<n>Our results demonstrate that FIPER better distinguishes actual failures from benign OOD situations and predicts failures more accurately and earlier than existing methods.
  arXiv  Detail & Related papers  (2025-10-10T15:09:27Z)
• AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation [24.199522837278128]
  We present a new notion of task-agnostic action paradigm that decouples action execution from task-specific conditioning.<n>ATARA is a scalable self-supervised framework that accelerates collection by over $ 30times $ compared to human teleoperation.<n>We propose AnyPos, an inverse dynamics model equipped with Arm-Decoupled Estimation and a Direction-Aware Decoder.
  arXiv  Detail & Related papers  (2025-07-17T03:48:57Z)
• AGENTSAFE: Benchmarking the Safety of Embodied Agents on Hazardous Instructions [64.85086226439954]
  We present SAFE, a benchmark for assessing the safety of embodied VLM agents on hazardous instructions.<n> SAFE comprises three components: SAFE-THOR, SAFE-VERSE, and SAFE-DIAGNOSE.<n>We uncover systematic failures in translating hazard recognition into safe planning and execution.
  arXiv  Detail & Related papers  (2025-06-17T16:37:35Z)
• BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization [45.97834622654751]
  BadVLA is a backdoor attack method based on Objective-Decoupled Optimization.<n>We show that BadVLA consistently achieves near-100% attack success rates with minimal impact on clean task accuracy.<n>Our work offers the first systematic investigation of backdoor vulnerabilities in VLA models.
  arXiv  Detail & Related papers  (2025-05-22T13:12:46Z)
• Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies [19.27526590452503]
  FAIL-Detect is a two-stage approach for failure detection in imitation learning-based robotic manipulation.<n>We first distill policy inputs and outputs into scalar signals that correlate with policy failures and capture uncertainty.<n>Our experiments show learned signals to be mostly consistently effective, particularly when using our novel flow-based density estimator.
  arXiv  Detail & Related papers  (2025-03-11T15:47:12Z)
• Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection [56.66677293607114]
  We propose Code-as-Monitor (CaM) for both open-set reactive and proactive failure detection.<n>To enhance the accuracy and efficiency of monitoring, we introduce constraint elements that abstract constraint-related entities.<n>Experiments show that CaM achieves a 28.7% higher success rate and reduces execution time by 31.8% under severe disturbances.
  arXiv  Detail & Related papers  (2024-12-05T18:58:27Z)
• GRAPE: Generalizing Robot Policy via Preference Alignment [58.419992317452376]
  We present GRAPE: Generalizing Robot Policy via Preference Alignment.<n>We show GRAPE increases success rates on in-domain and unseen manipulation tasks by 51.79% and 58.20%, respectively.<n> GRAPE can be aligned with various objectives, such as safety and efficiency, reducing collision rates by 37.44% and rollout step-length by 11.15%, respectively.
  arXiv  Detail & Related papers  (2024-11-28T18:30:10Z)
• Vision Language Models are In-Context Value Learners [89.29486557646624]
  We present Generative Value Learning (GVL), a universal value function estimator that leverages the world knowledge embedded in vision-language models (VLMs) to predict task progress. Without any robot or task specific training, GVL can in-context zero-shot and few-shot predict effective values for more than 300 distinct real-world tasks.
  arXiv  Detail & Related papers  (2024-11-07T09:17:50Z)
• Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress [31.952925824381325]
  We propose a runtime monitoring framework that splits the detection of failures into two complementary categories. We use Vision Language Models (VLMs) to detect when the policy confidently and consistently takes actions that do not solve the task. By unifying temporal consistency detection and VLM runtime monitoring, Sentinel detects 18% more failures than using either of the two detectors alone.
  arXiv  Detail & Related papers  (2024-10-06T22:13:30Z)
• Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces [5.993182776695029]
  We introduce Clip2Safety, an interpretable detection framework for diverse workplace safety compliance. The framework comprises four main modules: scene recognition, the visual prompt, safety items detection, and fine-grained verification. The results show that Clip2Safety not only demonstrates an accuracy improvement over state-of-the-art question-answering based VLMs but also achieves inference times two hundred times faster.
  arXiv  Detail & Related papers  (2024-08-13T18:32:06Z)
• Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism [120.1998866178014]
  We present a flexible framework for continual object detection via pRotOtypical taSk corrElaTion guided gaTingAnism (ROSETTA) Concretely, a unified framework is shared by all tasks while task-aware gates are introduced to automatically select sub-models for specific tasks. Experiments on COCO-VOC, KITTI-Kitchen, class-incremental detection on VOC and sequential learning of four tasks show that ROSETTA yields state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-05-06T07:31:28Z)
• Anomaly Detection in Video via Self-Supervised and Multi-Task Learning [113.81927544121625]
  Anomaly detection in video is a challenging computer vision problem. In this paper, we approach anomalous event detection in video through self-supervised and multi-task learning at the object level.
  arXiv  Detail & Related papers  (2020-11-15T10:21:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.