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• W4225873172 abstract "We tackle real-world long-horizon robot manipulation tasks through skill discovery. We present a bottom-up approach to learning a library of reusable skills from unsegmented demonstrations and use these skills to synthesize prolonged robot behaviors. Our method starts with constructing a hierarchical task structure from each demonstration through agglomerative clustering. From the task structures of multi-task demonstrations, we identify skills based on the recurring patterns and train goal-conditioned sensorimotor policies with hierarchical imitation learning. Finally, we train a meta controller to compose these skills to solve long-horizon manipulation tasks. The entire model can be trained on a small set of human demonstrations collected within 30 minutes without further annotations, making it amendable to real-world deployment. We systematically evaluated our method in simulation environments and on a real robot. Our method has shown superior performance over state-of-the-art imitation learning methods in multi-stage manipulation tasks. Furthermore, skills discovered from multi-task demonstrations boost the average task success by 8% compared to those discovered from individual tasks." @default.
• W4225873172 created "2022-05-05" @default.
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• W4225873172 date "2022-04-01" @default.
• W4225873172 modified "2023-10-18" @default.
• W4225873172 title "Bottom-Up Skill Discovery From Unsegmented Demonstrations for Long-Horizon Robot Manipulation" @default.
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• W4225873172 doi "https://doi.org/10.1109/lra.2022.3146589" @default.
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