Summary of Bridging the Resource Gap: Deploying Advanced Imitation Learning Models Onto Affordable Embedded Platforms, by Haizhou Ge et al.
Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms
by Haizhou Ge, Ruixiang Wang, Zhu-ang Xu, Hongrui Zhu, Ruichen Deng, Yuhang Dong, Zeyu Pang, Guyue Zhou, Junyu Zhang, Lu Shi
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a pipeline for deploying advanced imitation learning algorithms on embedded platforms, which is crucial for robotics applications. The approach combines model compression and asynchronous parallel processing using Temporal Ensemble with Dropped Actions (TEDA) to enhance the smoothness of operations. To demonstrate the effectiveness of this pipeline, large-scale imitation learning models are trained on a server and deployed on an edge device to complete various manipulation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make robots smarter by moving complex AI models from big computers to smaller devices like smartphones or smart home assistants. The authors developed a way to shrink these models while keeping them powerful enough for robots to learn new tasks. They tested this approach on various robot manipulation tasks and showed that it can work efficiently. |
Keywords
» Artificial intelligence » Model compression
