📄 中文摘要
研究提出了一种将机器人人工智能技术应用于嵌入式平台的方法，重点在于数据集的录制、变长注意力（VLA）的微调和设备上的优化。通过高效的数据集录制技术，能够收集到丰富的训练数据，进而提升模型的性能。VLA微调技术则使得模型在处理不同长度的输入时，能够更加灵活和高效。此外，针对嵌入式平台的硬件限制，研究还提出了一系列优化策略，以确保在资源受限的环境中，机器人AI能够实现实时响应和高效运行。这些方法的结合为嵌入式机器人系统的智能化提供了新的解决方案。
将机器人人工智能引入嵌入式平台：数据集录制、VLA微调和设备优化
📄 English Summary
Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations
The study presents a method for integrating robotics AI into embedded platforms, focusing on dataset recording, variable-length attention (VLA) fine-tuning, and on-device optimizations. Efficient dataset recording techniques enable the collection of rich training data, enhancing model performance. VLA fine-tuning allows models to be more flexible and efficient when handling inputs of varying lengths. Additionally, a series of optimization strategies are proposed to address the hardware limitations of embedded platforms, ensuring that robotics AI can achieve real-time responses and efficient operation in resource-constrained environments. The combination of these approaches offers new solutions for the intelligentization of embedded robotic systems.