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

• URL: http://arxiv.org/abs/2109.13841v1
• Date: Tue, 28 Sep 2021 16:18:54 GMT
• Title: Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation
• Authors: Yifeng Zhu, Peter Stone, Yuke Zhu
• Abstract summary: 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. Our method has shown superior performance over state-of-the-art imitation learning methods in multi-stage manipulation tasks.
• Score: 55.31301153979621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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