Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies (2025)

Abstract: Existing household robots have made significant progress in performing routine tasks, such as cleaning floors or delivering objects. However, a key limitation of these robots is their inability to recognize potential problems or dangers in home environments. For example, a child may pick up and ingest medication that has fallen on the floor, posing a serious risk. We argue that household robots should proactively detect such hazards or anomalies within the home, and propose the task of anomaly scenario generation. To accomplish this task, we leverage foundational models instead of relying on manually labeled data to build simulated environments. Specifically, we introduce a multi-agent brainstorming approach, where agents collaborate and generate diverse scenarios covering household hazards, hygiene management, and child safety. These textual task descriptions are then integrated with designed 3D assets to simulate realistic environments. Within these constructed environments, our LLM-based robotic agent learns the necessary skills to proactively discover and handle the proposed anomalies through task decomposition, optimal learning approach selection. We demonstrate that our generated environment outperforms others in terms of task description and scene diversity, ultimately enabling robotic agents to better address potential household hazards.

## introduction the development of vision-language models (vlms) has significantly improved household robots' ability to interact with the physical world in a more human-like manner @xcite @xcite . among these models, the most popular paradigm for such robots is receiving instructions and performing corresponding operational tasks @xcite @xcite . turn off the stove, otherwise there could be a fire hazard here. ## method in this section, we demonstrate how our framework utilizes advanced generative models to automatically create anomalous scenarios and task-related data, as shown in figure 2 . ## conclusion in this study, we present anomalygen, an innovative framework designed to enhance the proactive detection and resolution of household anomalies by robots. our approach integrates advanced generative models to automatically create diverse and realistic 3d environments, which are essential for training robots to handle real-world tasks autonomously. we also propose a group brainstorming method, which generates a wide variety of anomalous scenarios, surpassing traditional methods that rely heavily on manual input. furthermore, the anomalygen framework introduces a novel approach to anomaly detection, offering potential strategies for enabling robots to act without direct human commands. we hope our work will inspire further exploration into autonomous decision-making in real-world applications.