Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms
This work addresses the resource gap for robotics applications needing efficient edge deployment, but it is incremental as it builds on existing imitation learning and compression techniques.
The paper tackles the challenge of deploying large-scale imitation learning models on affordable embedded platforms by proposing a pipeline with model compression and an asynchronous parallel method, achieving successful deployment on an edge device for manipulation tasks.
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.