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ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
TLDR
We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. Expand
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RoboTHOR: An Open Simulation-to-Real Embodied AI Platform
TLDR
We introduce RoboTHOR to democratize research in interactive and embodied visual AI. Expand
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Artificial Agents Learn Flexible Visual Representations by Playing a Hiding Game
TLDR
We show that embodied adversarial reinforcement learning agents playing cache, a variant of hide-and-seek, in a high fidelity, interactive, environment, learn representations of their observations encoding information such as occlusion, object permanence, free space, and containment; on par with representations learnt by the most popular modern paradigm for visual representation learning which requires large datasets independently labeled for each new task. Expand
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Learning Generalizable Visual Representations via Interactive Gameplay
TLDR
We show that embodied adversarial reinforcement learning agents playing Cache, a variant of hide-and-seek, in a high fidelity, interactive, environment, learn generalizable representations of their observations encoding information such as object permanence, free space, and containment. Expand
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