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• W4285102371 abstract "We introduce a visually-guided task-and-motion planning benchmark, which we call the ThreeDWorld Trans-port Challenge. In this challenge, an embodied agent is spawned randomly in a simulated physical home environment and required to transport a small set of objects scattered around the house with containers. We build this benchmark challenge using the ThreeDWorld simulation: a virtual 3D environment where all objects respond to physics, and a robot agent can be controlled using a fully physics-driven navigation and interaction API. We evaluate several existing agents on this benchmark. Experimental results suggest that: 1) a pure RL model struggles on this challenge; 2) state-of-the-art hierarchical planning-based agents can transport some objects but are still far from solving this task. We anticipate that this benchmark will empower researchers to develop more intelligent physics-aware robot learning algorithms." @default.
• W4285102371 created "2022-07-14" @default.
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• W4285102371 date "2022-05-23" @default.
• W4285102371 modified "2023-10-05" @default.
• W4285102371 title "The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI" @default.
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• W4285102371 doi "https://doi.org/10.1109/icra46639.2022.9812329" @default.
• W4285102371 hasPublicationYear "2022" @default.
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