Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms

• URL: http://arxiv.org/abs/2411.11406v1
• Date: Mon, 18 Nov 2024 09:28:11 GMT
• Title: Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms
• Authors: Haizhou Ge, Ruixiang Wang, Zhu-ang Xu, Hongrui Zhu, Ruichen Deng, Yuhang Dong, Zeyu Pang, Guyue Zhou, Junyu Zhang, Lu Shi,
• Abstract summary: We propose a pipeline that facilitates the migration of advanced imitation learning algorithms to edge devices. 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.
• Score: 13.488752211167533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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