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

Xiaoya Lu^1,2*, Yijin Zhou^1,2*, Zeren Chen^3,1, Ruocheng Wang², Bingrui Sima⁴,
Enshen Zhou³, Lu Sheng³, Dongrui Liu^1✉, Jing Shao^1✉

¹Shanghai AI Laboratory, ²Shanghai Jiao Tong University,
³Beihang University, ⁴Huazhong University of Science and Technology

*Equal Contribution, ✉Corresponding authors

📰 News

• 2026.03.16 🤗🤗 We release our latest work HomeGuard, the first specialized embodied safeguard model for identifying contextual risk in household task.
• 🚀 Code release in progress! We are currently organizing the repository and will open-source it soon.

👀 Overview

• 🤖 Addressing Implicit Contextual Risks: While explicit malicious instructions are easier to detect, embodied agents often fail to identify implicit contextual risks—where benign instructions (e.g., "heat food") become hazardous due to environmental states (e.g., metal in a microwave).
• 🛡️ Architecture-Agnostic Safeguard: We propose HomeGuard, a plug-and-play safeguard that avoids complex rule-based systems. It uses Context-Guided Chain-of-Thought (CG-CoT) to decompose safety into active perception (prioritizing interaction targets) and semantic risk judgment.
• 🎯 Visual Anchors for Grounding: By equipping VLMs with visual anchors (bounding boxes), HomeGuard directs attention to risk-critical regions, effectively mitigating hallucinations and "unfocused perception" in cluttered, object-dense scenes.

Figure 1: Identifying implicit contextual risks via Context-Guided Chain-of-Thought.

Figure 2: An application case of HomeGuard facilitating safe trajectory generation.

📊 Performance

• 🚀 State-of-the-Art Risk Identification: HomeGuard-8B achieves a 90.98% RIR and 74.90% RMR on HomeSafe-Bench, significantly outperforming leading open-source models (Qwen3-VL-235B) and even matching or surpassing proprietary models like Gemini-3-Pro in complex embodied scenarios.
• 📉 Significant Reduction in Oversafety: By prioritizing hazard regions through active perception, HomeGuard reduces the oversafety rate by up to 19.48%, ensuring the agent remains functional without being overly cautious or "paranoid" due to perceptual noise.
• 🌍 Superior Generalization: Beyond our benchmark, HomeGuard demonstrates robust performance on four public risk identification benchmarks (EARBench, MSSBench, etc.), delivering results comparable to GPT-4o-mini and improving risk prediction accuracy by over 40% compared to base models.
• 🛠️ Practical Utility for Safe Planning: Integrating HomeGuard into VLM planners yields a 16.11% improvement on the IS-Bench safe success rate. Beyond semantic risk grounding, the generated bounding boxes serve as actionable spatial waypoints, enabling low-level safe trajectory generation.