COMPUTER VISION

ROBOTICS

Habitat 2.0: Training Home Assistants to Rearrange their Habitat

June 30, 2021

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 (850× real-time) on an 8-GPU node, representing 100× speed-ups over prior work; and, (iii) Home Assistant Benchmark (HAB): a suite of common tasks for assistive robots (tidy the house, stock 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

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AUTHORS

Written by

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

Publisher

Arxiv

Research Topics

Computer Vision

Robotics

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