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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.
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FR-AI / ANALYSISWhy it is worth attention
It implements a research paper from leading institutions (Stanford & Nvidia) that addresses context scaling in robot policy learning.
Who it is for
- Robotics researchers
- Machine learning practitioners
- Policy learning specialists
- AI engineers at research labs
Use cases
- Reproducing the RoboTTT paper results
- Building robot policies with context scaling
- Prototyping context-aware robotic systems
Strengths
- Direct implementation of a peer-reviewed research paper
- Backed by Stanford and Nvidia research groups
- Focuses on a specific, underexplored problem in robotics
Considerations
- Repository is marked as work-in-progress (wip), indicating incomplete or unpolished code
- README provides minimal documentation beyond the paper citation
- No code examples, usage instructions, or evaluation metrics are given
README quick start
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}
}
Description
Implementation of RoboTTT proposed by Yunfan Jiang et al. of Stanford and Nvidia
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