VLESA: Vision-Language Embodied Safety Agent for Human Activity Monitoring

• URL: http://arxiv.org/abs/2606.03954v1
• Date: Tue, 02 Jun 2026 17:42:17 GMT
• Title: VLESA: Vision-Language Embodied Safety Agent for Human Activity Monitoring
• Abstract summary: Vision-Language Embodied Safety Agent (VLESA) monitors human activities from egocentric video.<n>VLESA addresses intent-dependent safety where identical actions can be safe or dangerous depending on context.<n>An intent-action prediction agent is proposed to jointly infer goals and predict future actions from video.
• Score: 60.53395558502203
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As AI systems increasingly assist humans in physical tasks, ensuring safety becomes paramount -- physical actions carry immediate and irreversible consequences that digital errors do not. We introduce the Vision-Language Embodied Safety Agent (VLESA), a framework that monitors human activities from egocentric video and triggers real-time safety interventions when dangerous actions are predicted. VLESA addresses intent-dependent safety where identical actions can be safe or dangerous depending on context. A dataset pairing egocentric frames with goal-conditioned safety annotations is introduced, enabling a goal-conditioned safety Q-filter trained via GRPO that evaluates actions with respect to inferred intent without retraining. On top of that, an intent-action prediction agent is proposed to jointly infer goals and predict future actions from video. On the ASIMOV-2.0 benchmark, VLESA achieves higher intervention accuracy at the exact ground-truth frame compared to baselines, while the GRPO-trained Q-filter improves action safety by over 41 percentage points through goal-conditioned constrained decoding. Code is available at https://github.com/HanjiangHu/VLESA.

Related papers

• ForesightSafety-VLA: A Unified Diagnostic Safety Benchmark for Vision-Language-Action Models [7.1396544907031165]
  We introduce ForesightSafety-VLA, a diagnostic benchmark that makes safety the primary evaluation target for vision-trivial-action (VLA) systems.<n>We define a 13-category safety taxonomy covering physical interaction safety (Safe-Core), instruction-side safety (Safe-Lang), and perception-side safety (Safe-Vis)<n>We instantiate 66 safety-augmented base scenarios in RoboTwin across 5 embodiments and report results on representative VLA baselines.
  arXiv  Detail & Related papers  (2026-06-25T14:19:36Z)
• HazardArena: Evaluating Semantic Safety in Vision-Language-Action Models [87.35765363039638]
  We introduce HazardArena, a benchmark designed to evaluate semantic safety in Vision-Language-Action models.<n>We find that VLA models trained exclusively on safe scenarios often fail to behave safely when evaluated in their corresponding unsafe counterparts.<n>We propose a training-free Safety Option Layer that constrains action execution using semantic attributes or a vision-language judge.
  arXiv  Detail & Related papers  (2026-04-14T08:32:02Z)
• CORA: Conformal Risk-Controlled Agents for Safeguarded Mobile GUI Automation [68.53387633351484]
  We propose CORA (COnformal Risk-controlled GUI Agent), a post-policy, pre-action safeguarding framework that provides statistical guarantees on harmful executed actions.<n> CORA reformulates safety as selective action execution: we train a Guardian model to estimate action-conditional risk for each proposed step.<n>To rigorously evaluate this paradigm, we introduce Phone-Harm, a new benchmark of mobile safety violations with step-level harm labels.
  arXiv  Detail & Related papers  (2026-04-10T09:41:21Z)
• RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic [56.38397499463889]
  Embodied agents powered by vision-language models (VLMs) are increasingly capable of executing complex real-world tasks.<n>However, they remain vulnerable to hazardous instructions that may trigger unsafe behaviors.<n>We propose RoboSafe, a runtime safeguard for embodied agents through executable predicate-based safety logic.
  arXiv  Detail & Related papers  (2025-12-24T15:01:26Z)
• ManagerBench: Evaluating the Safety-Pragmatism Trade-off in Autonomous LLMs [48.50397204177239]
  As large language models (LLMs) evolve, evaluating the safety of their actions becomes critical.<n>We introduce ManagerBench, a benchmark that evaluates LLM decision-making in realistic, human-validated managerial scenarios.<n>A parallel control set, where potential harm is directed only at inanimate objects, measures a model's pragmatism and identifies its tendency to be overly safe.
  arXiv  Detail & Related papers  (2025-10-01T13:08:33Z)
• Defining and Evaluating Physical Safety for Large Language Models [62.4971588282174]
  Large Language Models (LLMs) are increasingly used to control robotic systems such as drones. Their risks of causing physical threats and harm in real-world applications remain unexplored. We classify the physical safety risks of drones into four categories: (1) human-targeted threats, (2) object-targeted threats, (3) infrastructure attacks, and (4) regulatory violations.
  arXiv  Detail & Related papers  (2024-11-04T17:41:25Z)
• ASMA: An Adaptive Safety Margin Algorithm for Vision-Language Drone Navigation via Scene-Aware Control Barrier Functions [9.645098673995317]
  We consider a VLN-operated drone platform and enhance its safety by formulating a novel scene-aware CBF.<n>A CBF-less baseline system uses a Vision-Language with cross-modal attention to convert commands into an ordered sequence of landmarks.<n>ASMA tracks moving objects and performs scene-aware CBF evaluation on-the-fly, which serves as an additional constraint.
  arXiv  Detail & Related papers  (2024-09-16T13:44:50Z)

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.