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

2024/11/18 by Huiyao Ge, Ruixiang Wang, Ge, Haizhou +14
Computer Science · #FOS: Computer and information sciences #Intelligent Tutoring Systems and Adaptive Learning #Machine Learning (cs.LG) #Online Learning and Analytics #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2411.11406

openalex publication_date 2024/11/18 · openalex created_date 2024/11/21 · openalex updated_date 2026/07/28

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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