slvDev
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esp32-ai
A 28.9 million parameter language model runs on an $8 ESP32-S3 microcontroller entirely on-device, generating simple stories at about 9.5 tokens per second.
AI 仓库情报摘要
FR-AI / ANALYSIS为什么值得关注
该项目实现了来自顶级机构（斯坦福 & 英伟达）的研究论文，专门解决机器人策略学习中的上下文缩放问题。
适合谁使用
- 机器人学研究人员
- 机器学习从业者
- 策略学习专家
- 研究实验室的 AI 工程师
典型使用场景
- 复现 RoboTTT 论文的实验结果
- 构建具备上下文缩放能力的机器人策略
- 原型验证上下文感知的机器人系统
项目优势
- 直接实现了经过同行评审的研究论文
- 得到斯坦福和英伟达研究团队的支持
- 专注于机器人学中一个特定且少有人探索的问题
使用前须知
- 仓库标记为 work-in-progress (wip)，代码可能不完整或未完善
- README 除论文引用外几乎没有文档说明
- 未提供代码示例、使用说明或评估指标
README 快速开始
RoboTTT (wip)
Implementation of RoboTTT proposed by Yunfan Jiang et al. of Stanford and Nvidia
Citations
@article{jiang2026robottt0,
title = {RoboTTT: Context Scaling for Robot Policies},
author = {Yunfan Jiang and Yevgen Chebotar and Ruijie Zheng and Fengyuan Hu and Yunhao Ge and Jimmy Wu and Tianyuan Dai and Scott Reed and Li Fei-Fei and Yuke Zhu and Linxi "Jim" Fan},
year = {2026},
journal = {arXiv preprint arXiv: 2607.15275}
}
项目描述
Implementation of RoboTTT proposed by Yunfan Jiang et al. of Stanford and Nvidia
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