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We introduce Habitat 2.0 (H2.0), a simulation platform for training virtualrobots in interactive 3D environments and complex physics-enabled scenarios. Wemake comprehensive contributions to all levels of the embodied AI stack - data,simulation, and benchmark tasks. Specifically, we present: (i) ReplicaCAD: anartist-authored, annotated, reconfigurable 3D dataset of apartments (matchingreal spaces) with articulated objects (e.g. cabinets and drawers that canopen/close); (ii) H2.0: a high-performance physics-enabled 3D simulator withspeeds exceeding 25,000 simulation steps per second (850x real-time) on an8-GPU node, representing 100x speed-ups over prior work; and, (iii) HomeAssistant Benchmark (HAB): a suite of common tasks for assistive robots (tidythe house, prepare groceries, set the table) that test a range of mobilemanipulation capabilities. These large-scale engineering contributions allow usto systematically compare deep reinforcement learning (RL) at scale andclassical sense-plan-act (SPA) pipelines in long-horizon structured tasks, withan emphasis on generalization to new objects, receptacles, and layouts. We findthat (1) flat RL policies struggle on HAB compared to hierarchical ones; (2) ahierarchy with independent skills suffers from 'hand-off problems', and (3) SPApipelines are more brittle than RL policies.

Habitat 2.0: Training Home Assistants to Rearrange their Habitat