将复杂模仿学习模型部署到低成本嵌入式设备上
Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms
- 采用高效模型压缩与异步并行机制实现迁移
- 在边缘设备上成功完成多种操作任务
- 适合资源受限场景下的机器人控制应用
具有 Transformer 结构的先进模仿学习在机器人领域日益展现出优势。然而，将这些大规模模型部署到嵌入式平台仍面临重大挑战。本文提出一个流程，可促进先进模仿学习算法向边缘设备迁移。该流程通过高效的模型压缩方法和实用的异步并行方法——时间集成丢弃动作（TEDA），提升操作平滑性。为验证所提流程的效率，大型模仿学习模型在服务器上训练后，被部署于边缘设备，成功完成多种操纵任务。
原文摘要 · Abstract (English)
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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