HomeGuard: VLM-based Embodied Safeguard for Identifying Contextual Risk in Household Task

Vision-Language Models (VLMs) empower embodied agentsto execute complex instructions, yet they remain vulnerable to contex-tual safety risks where benign commands become hazardous due to subtleenvironmental states. Existing safeguards often prove inadequate. Rule-based methods lack scalability in object-dense scenes, whereas model-based approaches relying on prompt engineering suffer from unfocusedperception, resulting in missed risks or hallucinations. To address this,we propose an architecture-agnostic safeguard featuring Context-GuidedChain-of-Thought (CG-CoT). This mechanism decomposes risk assess-ment into active perception that sequentially anchors attention to inter-action targets and relevant spatial neighborhoods, followed by semanticjudgment based on this visual evidence. We support this approach witha curated grounding dataset and a two-stage training strategy utilizingReinforcement Fine-Tuning (RFT) with process rewards to enforce pre-cise intermediate grounding. Experiments demonstrate that our modelsignificantly enhances safety, improving risk match rates by over 30%compared to base models while reducing oversafety. Beyond hazard de-tection, the generated visual anchors serve as actionable spatial con-straints for downstream planners, facilitating explicit collision avoidanceand safety trajectory generation.