Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms
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arXiv
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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915024527687680 |
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| author | Ge, Haizhou Wang, Ruixiang Xu, Zhu-ang Zhu, Hongrui Deng, Ruichen Dong, Yuhang Pang, Zeyu Zhou, Guyue Zhang, Junyu Shi, Lu |
| author_facet | Ge, Haizhou Wang, Ruixiang Xu, Zhu-ang Zhu, Hongrui Deng, Ruichen Dong, Yuhang Pang, Zeyu Zhou, Guyue Zhang, Junyu Shi, Lu |
| contents | Advanced imitation learning with structures like the transformer is increasingly demonstrating its advantages in robotics. However, deploying these large-scale models on embedded platforms remains a major challenge. In this paper, we propose a pipeline that facilitates the migration of advanced imitation learning algorithms to edge devices. The process is achieved via an efficient model compression method and a practical asynchronous parallel method Temporal Ensemble with Dropped Actions (TEDA) that enhances the smoothness of operations. To show the efficiency of the proposed pipeline, large-scale imitation learning models are trained on a server and deployed on an edge device to complete various manipulation tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11406 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms Ge, Haizhou Wang, Ruixiang Xu, Zhu-ang Zhu, Hongrui Deng, Ruichen Dong, Yuhang Pang, Zeyu Zhou, Guyue Zhang, Junyu Shi, Lu Machine Learning Robotics Advanced imitation learning with structures like the transformer is increasingly demonstrating its advantages in robotics. However, deploying these large-scale models on embedded platforms remains a major challenge. In this paper, we propose a pipeline that facilitates the migration of advanced imitation learning algorithms to edge devices. The process is achieved via an efficient model compression method and a practical asynchronous parallel method Temporal Ensemble with Dropped Actions (TEDA) that enhances the smoothness of operations. To show the efficiency of the proposed pipeline, large-scale imitation learning models are trained on a server and deployed on an edge device to complete various manipulation tasks. |
| title | Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms |
| topic | Machine Learning Robotics |
| url | https://arxiv.org/abs/2411.11406 |