Habitat 2.0: Training Home Assistants to Rearrange their Habitat
Not on the map. Why?
Cited by 36 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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.
Key facts. Cited by 511 papers in the map (data through 14 Oct 2026). Builds on 67 of its 124 references that are in the map. First posted on arXiv 28 Jun 2021.
Builds on
67 of 124 references.
References · 67
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38 more
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- 2020Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixelscited by 585
- 2018On Evaluation of Embodied Navigation Agentscited by 547
- 2018VirtualHome: Simulating Household Activities via Programscited by 415
- 2020Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Groundingcited by 414
- 2019DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Framescited by 362
- 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 319
- 2020Integrated Task and Motion Planningcited by 290
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- 2020Rearrangement: A Challenge for Embodied AIcited by 155
- 2020iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenescited by 155
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- 2018Building Generalizable Agents with a Realistic and Rich 3D Environmentcited by 109
- 2013Data-Driven Grasp Synthesis - A Surveycited by 106
- 2020Occupancy Anticipation for Efficient Exploration and Navigationcited by 106
- 2021Visual Room Rearrangementcited by 102
- 2021ManipulaTHOR: A Framework for Visual Object Manipulationcited by 102
- 2018Mapping Instructions to Actions in 3D Environments with Visual Goal Predictioncited by 101
- 2020Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learningcited by 93
- 2019Interactive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environmentscited by 90
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- 2017Learning Sampling Distributions for Robot Motion Planningcited by 82
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- 2020OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasetscited by 74
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- 2017MINOS: Multimodal Indoor Simulator for Navigation in Complex Environmentscited by 66
- 2021The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AIcited by 65
- 2019Online Replanning in Belief Space for Partially Observable Task and Motion Problemscited by 61
- 2019Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learningcited by 60
- 2020Auxiliary Tasks Speed Up Learning PointGoal Navigationcited by 54
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50 most cited of 67 All 67 (JSON)
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Citing papers per year: 2021: 17; 2022: 65; 2023: 110; 2024: 100; 2025: 127; 2026: 92.
511 papers.
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