  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.
