2022
DOI: 10.48550/arxiv.2202.06674
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Learning to Ground Objects for Robot Task and Motion Planning

Abstract: Task and motion planning (TAMP) algorithms have been developed to help robots plan behaviors in discrete and continuous spaces. Robots face complex real-world scenarios, where it is hardly possible to model all objects or their physical properties for robot planning (e.g., in kitchens or shopping centers). In this paper, we define a new object-centric TAMP problem, where the TAMP robot does not know object properties (e.g., size and weight of blocks). We then introduce Task-Motion Object-Centric planning (TMOC… Show more

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References 26 publications
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