1. Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms
- Author
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Ge, Haizhou, Wang, Ruixiang, Xu, Zhu-ang, Zhu, Hongrui, Deng, Ruichen, Dong, Yuhang, Pang, Zeyu, Zhou, Guyue, Zhang, Junyu, and Shi, Lu
- Subjects
Computer Science - Machine Learning ,Computer Science - Robotics - Abstract
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., Comment: Accepted by the 2024 IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2024)
- Published
- 2024