# Ultimate Robotics

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Kseniia Kyrylyshena](https://cerebralvalley.ai/u/paladin)
- **GitHub:** https://github.com/ultimaterobotics/camera-autolabeler
- **Demo video:** https://www.youtube.com/watch?v=lHY-8xWjjfw
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/291

Manual labeling is the bottleneck of biosignal ML — every hour of EMG needs a human marking what the muscles were doing. We built a tool that removes the human: a first-person camera watches the hands, a pose model turns the video into timestamped gesture labels, and any instrument recording on the same clock — EMG in our lab, ECG or IMU in yours — inherits those labels for free. The camera is a teacher, not the product. During the event the loop closed: a pretrained hand model labeled 320 frames of our own footage (zero human keypoints), a small pose model fine-tuned on them and outgrew its teacher — 71% vs 46% — and now labels its own next dataset. On a consumer AMD GPU each fine-tune takes minutes, so labeling improves between recordings. Standalone, sensor-agnostic, open source: a session folder in, two CSVs out.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/291. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt