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- W4387293604 abstract "Stowing, the task of placing objects in cluttered shelves or bins, is a common task in warehouse and manufacturing operations. However, this task is still predominantly carried out by human workers as stowing is challenging to automate due to the complex multi-object interactions and long-horizon nature of the task. Previous works typically involve extensive data collection and costly human labeling of semantic priors across diverse object categories. This paper presents a method to learn a generalizable robot stowing policy from predictive model of object interactions and a single demonstration with behavior primitives. We propose a novel framework that utilizes Graph Neural Networks to predict object interactions within the parameter space of behavioral primitives. We further employ primitive-augmented trajectory optimization to search the parameters of a predefined library of heterogeneous behavioral primitives to instantiate the control action. Our framework enables robots to proficiently execute long-horizon stowing tasks with a few keyframes (3-4) from a single demonstration. Despite being solely trained in a simulation, our framework demonstrates remarkable generalization capabilities. It efficiently adapts to a broad spectrum of real-world conditions, including various shelf widths, fluctuating quantities of objects, and objects with diverse attributes such as sizes and shapes." @default.
- W4387293604 created "2023-10-03" @default.
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- W4387293604 date "2023-09-28" @default.
- W4387293604 modified "2023-10-04" @default.
- W4387293604 title "Predicting Object Interactions with Behavior Primitives: An Application in Stowing Tasks" @default.
- W4387293604 doi "https://doi.org/10.48550/arxiv.2309.16873" @default.
- W4387293604 hasPublicationYear "2023" @default.
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