LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916914745311232 |
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| author | Wan, Weikang Fu, Jiawei Yuan, Xiaodi Zhu, Yifeng Su, Hao |
| author_facet | Wan, Weikang Fu, Jiawei Yuan, Xiaodi Zhu, Yifeng Su, Hao |
| contents | Developing robotic systems capable of robustly executing long-horizon manipulation tasks with human-level dexterity is challenging, as such tasks require both physical dexterity and seamless sequencing of manipulation skills while robustly handling environment variations. While imitation learning offers a promising approach, acquiring comprehensive datasets is resource-intensive. In this work, we propose a learning framework and system LodeStar that automatically decomposes task demonstrations into semantically meaningful skills using off-the-shelf foundation models, and generates diverse synthetic demonstration datasets from a few human demos through reinforcement learning. These sim-augmented datasets enable robust skill training, with a Skill Routing Transformer (SRT) policy effectively chaining the learned skills together to execute complex long-horizon manipulation tasks. Experimental evaluations on three challenging real-world long-horizon dexterous manipulation tasks demonstrate that our approach significantly improves task performance and robustness compared to previous baselines. Videos are available at lodestar-robot.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17547 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations Wan, Weikang Fu, Jiawei Yuan, Xiaodi Zhu, Yifeng Su, Hao Robotics Artificial Intelligence Machine Learning Developing robotic systems capable of robustly executing long-horizon manipulation tasks with human-level dexterity is challenging, as such tasks require both physical dexterity and seamless sequencing of manipulation skills while robustly handling environment variations. While imitation learning offers a promising approach, acquiring comprehensive datasets is resource-intensive. In this work, we propose a learning framework and system LodeStar that automatically decomposes task demonstrations into semantically meaningful skills using off-the-shelf foundation models, and generates diverse synthetic demonstration datasets from a few human demos through reinforcement learning. These sim-augmented datasets enable robust skill training, with a Skill Routing Transformer (SRT) policy effectively chaining the learned skills together to execute complex long-horizon manipulation tasks. Experimental evaluations on three challenging real-world long-horizon dexterous manipulation tasks demonstrate that our approach significantly improves task performance and robustness compared to previous baselines. Videos are available at lodestar-robot.github.io. |
| title | LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.17547 |