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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.01089 |
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| _version_ | 1866910920830091264 |
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| author | Mullen Jr, James F. Kumar, Dhruva Qi, Xuewei Madhivanan, Rajasimman Sen, Arnie Manocha, Dinesh Kim, Richard |
| author_facet | Mullen Jr, James F. Kumar, Dhruva Qi, Xuewei Madhivanan, Rajasimman Sen, Arnie Manocha, Dinesh Kim, Richard |
| contents | In the United States alone accidental home deaths exceed 128,000 per year. Our work aims to enable home robots who respond to emergency scenarios in the home, preventing injuries and deaths. We introduce a new dataset of household emergencies based in the ThreeDWorld simulator. Each scenario in our dataset begins with an instantaneous or periodic sound which may or may not be an emergency. The agent must navigate the multi-room home scene using prior observations, alongside audio signals and images from the simulator, to determine if there is an emergency or not.
In addition to our new dataset, we present a modular approach for localizing and identifying potential home emergencies. Underpinning our approach is a novel probabilistic dynamic scene graph (P-DSG), where our key insight is that graph nodes corresponding to agents can be represented with a probabilistic edge. This edge, when refined using Bayesian inference, enables efficient and effective localization of agents in the scene. We also utilize multi-modal vision-language models (VLMs) as a component in our approach, determining object traits (e.g. flammability) and identifying emergencies. We present a demonstration of our method completing a real-world version of our task on a consumer robot, showing the transferability of both our task and our method. Our dataset will be released to the public upon this papers publication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01089 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HomeEmergency -- Using Audio to Find and Respond to Emergencies in the Home Mullen Jr, James F. Kumar, Dhruva Qi, Xuewei Madhivanan, Rajasimman Sen, Arnie Manocha, Dinesh Kim, Richard Robotics Artificial Intelligence In the United States alone accidental home deaths exceed 128,000 per year. Our work aims to enable home robots who respond to emergency scenarios in the home, preventing injuries and deaths. We introduce a new dataset of household emergencies based in the ThreeDWorld simulator. Each scenario in our dataset begins with an instantaneous or periodic sound which may or may not be an emergency. The agent must navigate the multi-room home scene using prior observations, alongside audio signals and images from the simulator, to determine if there is an emergency or not. In addition to our new dataset, we present a modular approach for localizing and identifying potential home emergencies. Underpinning our approach is a novel probabilistic dynamic scene graph (P-DSG), where our key insight is that graph nodes corresponding to agents can be represented with a probabilistic edge. This edge, when refined using Bayesian inference, enables efficient and effective localization of agents in the scene. We also utilize multi-modal vision-language models (VLMs) as a component in our approach, determining object traits (e.g. flammability) and identifying emergencies. We present a demonstration of our method completing a real-world version of our task on a consumer robot, showing the transferability of both our task and our method. Our dataset will be released to the public upon this papers publication. |
| title | HomeEmergency -- Using Audio to Find and Respond to Emergencies in the Home |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2504.01089 |