{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/habitat-2-0-training-home-assistants-to","title":"Habitat 2.0: Training Home Assistants to Rearrange their Habitat","arxiv_id":"2106.14405","date":"2021-06-28","proceeding":"NeurIPS 2021 12","authors":["Andrew Szot","Alex Clegg","Eric Undersander","Erik Wijmans","Yili Zhao","John Turner","Noah Maestre","Mustafa Mukadam","Devendra Chaplot","Oleksandr Maksymets","Aaron Gokaslan","Vladimir Vondrus","Sameer Dharur","Franziska Meier","Wojciech Galuba","Angel Chang","Zsolt Kira","Vladlen Koltun","Jitendra Malik","Manolis Savva","Dhruv Batra"],"abstract":"We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios. We make comprehensive contributions to all levels of the embodied AI stack - data, simulation, and benchmark tasks. Specifically, we present: (i) ReplicaCAD: an artist-authored, annotated, reconfigurable 3D dataset of apartments (matching real spaces) with articulated objects (e.g. cabinets and drawers that can open/close); (ii) H2.0: a high-performance physics-enabled 3D simulator with speeds exceeding 25,000 simulation steps per second (850x real-time) on an 8-GPU node, representing 100x speed-ups over prior work; and, (iii) Home Assistant Benchmark (HAB): a suite of common tasks for assistive robots (tidy the house, prepare groceries, set the table) that test a range of mobile manipulation capabilities. These large-scale engineering contributions allow us to systematically compare deep reinforcement learning (RL) at scale and classical sense-plan-act (SPA) pipelines in long-horizon structured tasks, with an emphasis on generalization to new objects, receptacles, and layouts. We find that (1) flat RL policies struggle on HAB compared to hierarchical ones; (2) a hierarchy with independent skills suffers from 'hand-off problems', and (3) SPA pipelines are more brittle than RL policies.","url_abs":"https://arxiv.org/abs/2106.14405v2","url_pdf":"https://arxiv.org/pdf/2106.14405v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"habitat-2-0-training-home-assistants-to","repo_url":"https://github.com/facebookresearch/habitat-lab","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"habitat-2-0-training-home-assistants-to","repo_url":"https://github.com/ericchen321/ros_x_habitat","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"habitat-2-0-training-home-assistants-to","repo_url":"https://github.com/facebookresearch/habitat-api","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"habitat-2-0-training-home-assistants-to","repo_url":"https://github.com/facebookresearch/habitat-challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"habitat-2-0-training-home-assistants-to","repo_url":"https://github.com/facebookresearch/habitat-sim","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"habitat-2-0-training-home-assistants-to","repo_url":"https://github.com/super5-22/habitat-vis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"single-particle-analysis","task_name":"Single Particle Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2106.14405","atlas_url":"https://app.syntology.ai/?focus=2106.14405","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.14405"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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