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AI2-THOR: An Interactive 3D Environment for Visual AI

AI2-THOR consists of near photo-realistic 3D indoor scenes, where AI agents can navigate in the scenes and interact with objects to perform tasks and facilitate building visually intelligent models.
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Target-driven visual navigation in indoor scenes using deep reinforcement learning

This paper proposes an actor-critic model whose policy is a function of the goal as well as the current state, which allows better generalization and proposes the AI2-THOR framework, which provides an environment with high-quality 3D scenes and a physics engine.
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A Diagram is Worth a Dozen Images

An LSTM-based method for syntactic parsing of diagrams and a DPG-based attention model for diagram question answering are devised and a new dataset of diagrams with exhaustive annotations of constituents and relationships is compiled.
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RoboTHOR: An Open Simulation-to-Real Embodied AI Platform

RoboTHOR offers a framework of simulated environments paired with physical counterparts to systematically explore and overcome the challenges of simulation-to-real transfer, and a platform where researchers across the globe can remotely test their embodied models in the physical world.
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Visual Semantic Planning Using Deep Successor Representations

This work addresses the problem of visual semantic planning: the task of predicting a sequence of actions from visual observations that transform a dynamic environment from an initial state to a goal state, and develops a deep predictive model based on successor representations.
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A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks

The novel task FurnMove is introduced, in which agents work together to move a piece of furniture through a living room to a goal, and SYNC-policies (synchronize your actions coherently) and CORDIAL (coordination loss) are introduced.
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CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents

This work presents CORA, a platform for Continual Reinforcement Learning Agents that provides benchmarks, baselines, and metrics in a single code package, and hopes that the continual RL community can benefit from contributions, to accelerate the development of new continual RL algorithms.
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ManipulaTHOR: A Framework for Visual Object Manipulation

This work proposes a framework for object manipulation built upon the physics-enabled, visually rich AI2-THOR framework and presents a new challenge to the Embodied AI community known as ArmPointNav, which extends the popular point navigation task to object manipulation and offers new challenges including 3D obstacle avoidance.
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Two Body Problem: Collaborative Visual Task Completion

This paper studies the problem of learning to collaborate directly from pixels in AI2-THOR and demonstrates the benefits of explicit and implicit modes of communication to perform visual tasks.
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Webly Supervised Concept Expansion for General Purpose Vision Models

This work uses a dataset of 1M+ images spanning 10k+ visual concepts to demonstrate webly-supervised concept expansion for two existing GPVs and proposes a new architecture, GPV-2, that supports a variety of tasks — from vision tasks like classification and localization to vision+language tasks like QA and captioning, to more niche ones like human-object interaction detection.
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