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Competition
Habitat Rearrangement Challenge
Andrew Szot · Karmesh Yadav · Alexander Clegg · Vincent-Pierre Berges · Aaron Gokaslan · Angel Chang · Manolis Savva · Zsolt Kira · Dhruv Batra

Thu Dec 08 01:00 PM -- 04:00 PM (PST) @ Virtual

We propose the Habitat Rearrangement Challenge. Specifically, a virtual robot (Fetch mobile manipulator) is spawned in a previously unseen simulation environment and asked to rearrange objects from initial to desired positions -- picking/placing objects from receptacles (counter, sink, sofa, table), opening/closing containers (drawers, fridges) as necessary. The robot operates entirely from onboard sensing -- head- and arm-mounted RGB-D cameras, proprioceptive joint-position sensors (for the arm), and egomotion sensors (for the mobile base) -- and may not access any privileged state information (no prebuilt maps, no 3D models of rooms or objects, no physically-implausible sensors providing knowledge of mass, friction, articulation of containers). This is a challenging embodied AI task involving embodied perception, mobile manipulation, sequential decision making in long-horizon tasks, and (potentially) deep reinforcement and imitation learning. Developing such embodied intelligent systems is a goal of deep scientific and societal value, including practical applications in home assistant robots.

Author Information

Andrew Szot (Georgia Institute of Technology)
Karmesh Yadav (Carnegie)
Alexander Clegg (Facebook (FAIR Labs))
Vincent-Pierre Berges (Facebook AI Research)
Aaron Gokaslan (Cornell)
Angel Chang (Simon Fraser University)
Manolis Savva (Simon Fraser University)
Zsolt Kira (Georgia Institute of Techology)
Dhruv Batra (FAIR (Meta) / Georgia Tech)

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