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arXiv · 2109.13841

Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation

Yifeng Zhu·Peter Stone·Yuke Zhu
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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.

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Yifeng Zhu, Peter Stone, Yuke Zhu. 2021-09-28. Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation. https://arxiv.org/abs/2109.13841

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