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- W4362706591 abstract "We focus on the task of language-conditioned object placement, in which a robot should generate placements that satisfy all the spatial relational constraints in language instructions. Previous works based on rule-based language parsing or scene-centric visual representation have restrictions on the form of instructions and reference objects or require large amounts of training data. We propose an object-centric framework that leverages foundation models to ground the reference objects and spatial relations for placement, which is more sample efficient and generalizable. Experiments indicate that our model can achieve a 97.75% success rate of placement with only ~0.26M trainable parameters. Besides, our method generalizes better to both unseen objects and instructions. Moreover, with only 25% training data, we still outperform the top competing approach." @default.
- W4362706591 created "2023-04-09" @default.
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- W4362706591 date "2023-04-06" @default.
- W4362706591 modified "2023-09-26" @default.
- W4362706591 title "Object-centric Inference for Language Conditioned Placement: A Foundation Model based Approach" @default.
- W4362706591 doi "https://doi.org/10.48550/arxiv.2304.02893" @default.
- W4362706591 hasPublicationYear "2023" @default.
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