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docs(agents): add AGENT_GUIDE.md for user facing agent (#3430)

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* docs(agents): add AGENT_GUIDE.md with SO-101, data, policy, training, eval guidance

Adds an agent-facing companion to AGENTS.md that helps AI agents (Cursor,
Claude, ChatGPT, etc.) guide end-users through LeRobot without needing to
re-read every doc:

- Mandatory "ask the user first" block (goal, hardware, GPU, skill level)
- SO-101 end-to-end cheat-sheet: install -> calibrate -> record -> train -> eval
- Data-collection tips distilled from the folding project (practice before
  you record, quality > speed, start constrained then add diversity)
- Policy decision table with indicative profiling numbers (update ms, peak
  GPU mem) and AdamW-vs-SGD caveats
- Training duration guidance: 5-10 epoch rule, epoch<->step conversion,
  scheduler/checkpoint scaling with --steps, SmolVLA unfreeze tip
- Real-robot eval via lerobot-record --policy.path and sim eval via
  lerobot-eval, including the pre-baked docker/Dockerfile.benchmark.* images

AGENTS.md gets a short pointer to AGENT_GUIDE.md at the top.
CLAUDE.md (symlink to AGENTS.md) inherits the pointer automatically.

Made-with: Cursor

* docs(agents): recommend 2 cameras (front + wrist) as default

Made-with: Cursor

* docs(agents): add Feetech wiring check and broaden visualizer note

Made-with: Cursor

* docs(agents): clarify Feetech LED behavior (steady-on, not flash)

Made-with: Cursor

* docs(agents): expand Feetech troubleshooting (blinking LED, 5V vs 12V variants)

Made-with: Cursor

* docs(agents): tighten Feetech LED wording

Made-with: Cursor
This commit is contained in:
Pepijn
2026-04-22 11:54:19 +02:00
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parent ba27aab79c
commit 39b966e20a
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AGENTS.md
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This file provides guidance to AI agents when working with code in this repository.
> **User-facing help → [`AGENT_GUIDE.md`](./AGENT_GUIDE.md)** (SO-101 setup, recording, picking a policy, training duration, eval — with copy-pasteable commands).
## Project Overview
LeRobot is a PyTorch-based library for real-world robotics, providing datasets, pretrained policies, and tools for training, evaluation, data collection, and robot control. It integrates with Hugging Face Hub for model/dataset sharing.