{"url":"/dataset/home","name":"HoME","full_name":"Household Multimodal Environment","description_markdown":"HoME (Household Multimodal Environment) is a 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.","description_withheld":null,"homepage":"https://arxiv.org/pdf/1711.11017v1.pdf","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/home-a-household-multimodal-environment","title":"HoME: a Household Multimodal Environment","first_author":"Simon Brodeur","url":null},"license":{"name":"Unknown","url":null},"modalities":[],"tasks":[],"languages":[],"variants":["HoME"],"data_loaders":[],"num_papers_in_archive":22,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}