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- W4308756558 abstract "Robots operating in human environments must be able to rearrange objects into semantically-meaningful configurations, even if these objects are previously unseen. In this work, we focus on the problem of building physically-valid structures without step-by-step instructions. We propose StructDiffusion, which combines a diffusion model and an object-centric transformer to construct structures given partial-view point clouds and high-level language goals, such as set the table. Our method can perform multiple challenging language-conditioned multi-step 3D planning tasks using one model. StructDiffusion even improves the success rate of assembling physically-valid structures out of unseen objects by on average 16% over an existing multi-modal transformer model trained on specific structures. We show experiments on held-out objects in both simulation and on real-world rearrangement tasks. Importantly, we show how integrating both a diffusion model and a collision-discriminator model allows for improved generalization over other methods when rearranging previously-unseen objects. For videos and additional results, see our website: https://structdiffusion.github.io/." @default.
- W4308756558 created "2022-11-15" @default.
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- W4308756558 date "2022-11-08" @default.
- W4308756558 modified "2023-09-29" @default.
- W4308756558 title "StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects" @default.
- W4308756558 doi "https://doi.org/10.48550/arxiv.2211.04604" @default.
- W4308756558 hasPublicationYear "2022" @default.
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