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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.14795 |
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| _version_ | 1866913251427614720 |
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| author | Wang, Jun Qin, Yuzhe Kuang, Kaiming Korkmaz, Yigit Gurumoorthy, Akhilan Su, Hao Wang, Xiaolong |
| author_facet | Wang, Jun Qin, Yuzhe Kuang, Kaiming Korkmaz, Yigit Gurumoorthy, Akhilan Su, Hao Wang, Xiaolong |
| contents | We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks. By incorporating extensive data augmentation in a simulated environment, CyberDemo outperforms traditional in-domain real-world demonstrations when transferred to the real world, handling diverse physical and visual conditions. Regardless of its affordability and convenience in data collection, CyberDemo outperforms baseline methods in terms of success rates across various tasks and exhibits generalizability with previously unseen objects. For example, it can rotate novel tetra-valve and penta-valve, despite human demonstrations only involving tri-valves. Our research demonstrates the significant potential of simulated human demonstrations for real-world dexterous manipulation tasks. More details can be found at https://cyber-demo.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14795 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation Wang, Jun Qin, Yuzhe Kuang, Kaiming Korkmaz, Yigit Gurumoorthy, Akhilan Su, Hao Wang, Xiaolong Robotics Computer Vision and Pattern Recognition We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks. By incorporating extensive data augmentation in a simulated environment, CyberDemo outperforms traditional in-domain real-world demonstrations when transferred to the real world, handling diverse physical and visual conditions. Regardless of its affordability and convenience in data collection, CyberDemo outperforms baseline methods in terms of success rates across various tasks and exhibits generalizability with previously unseen objects. For example, it can rotate novel tetra-valve and penta-valve, despite human demonstrations only involving tri-valves. Our research demonstrates the significant potential of simulated human demonstrations for real-world dexterous manipulation tasks. More details can be found at https://cyber-demo.github.io |
| title | CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.14795 |