docs: add project README
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README.md
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---
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## How to Get Started with the Model
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For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
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Below is the short version on how to train and run inference/eval:
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### Train from scratch
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```bash
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lerobot-train \
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--dataset.repo_id=${HF_USER}/<dataset> \
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--policy.type=act \
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--output_dir=outputs/train/<desired_policy_repo_id> \
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--job_name=lerobot_training \
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--policy.device=cuda \
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--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
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--wandb.enable=true
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```
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_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
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### Evaluate the policy/run inference
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```bash
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lerobot-record \
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--robot.type=so100_follower \
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--dataset.repo_id=<hf_user>/eval_<dataset> \
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--policy.path=<hf_user>/<desired_policy_repo_id> \
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--episodes=10
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```
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Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
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---
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## Model Details
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- **License:** apache-2.0
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# enhanced-inverse-aug
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## 說明
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- **專案**: lerobot-vla-fbagent (FB Native VLA for robotic manipulation)
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- **版本**: enhanced
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- **Policy type**: joint_enhanced_act
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- **用途**: 實驗性方法 (FiLM enhancer)
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## 訓練參數
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- Dataset: HuggingFaceVLA/libero
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- Seed: 42
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- Steps: 5000
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- Batch size: 32
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- Hardware: MSI RTX 5090 Laptop 24GB
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## 狀態
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- 已完成 (實驗性，未列入論文)
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- 未 eval
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## 相關
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- GitHub: KunHsiang/lerobot-vla-fbagent
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- W&B: N/A
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