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HoME: a Household Multimodal Environment
Abstract
International audienceWe introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting- info:eu-repo/semantics/conferenceObject
- Conference papers
- [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]
- [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
- [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
- [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO]
- [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD]