docs: add project README

README.md

@@ -1,62 +1,22 @@
----
-datasets: HuggingFaceVLA/libero
-library_name: lerobot
-license: apache-2.0
-model_name: joint_fb_act
-pipeline_tag: robotics
-tags:
-- lerobot
-- robotics
-- joint_fb_act
----
-
-# Model Card for joint_fb_act
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-_Model type not recognized — please update this template._
-
-
-This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
-See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
-
----
-
-## How to Get Started with the Model
-
-For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
-Below is the short version on how to train and run inference/eval:
-
-### Train from scratch
-
-```bash
-lerobot-train \
-  --dataset.repo_id=${HF_USER}/<dataset> \
-  --policy.type=act \
-  --output_dir=outputs/train/<desired_policy_repo_id> \
-  --job_name=lerobot_training \
-  --policy.device=cuda \
-  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
-  --wandb.enable=true
-```
-
-_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
-
-### Evaluate the policy/run inference
-
-```bash
-lerobot-record \
-  --robot.type=so100_follower \
-  --dataset.repo_id=<hf_user>/eval_<dataset> \
-  --policy.path=<hf_user>/<desired_policy_repo_id> \
-  --episodes=10
-```
-
-Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
-
----
-
-## Model Details
-
-- **License:** apache-2.0
+# joint-fb-act-test
+
+## 說明
+- **專案**: lerobot-vla-fbagent (FB Native VLA for robotic manipulation)
+- **版本**: v1
+- **Policy type**: joint_fb_act
+- **用途**: Paper 2 v1 早期探索
+
+## 訓練參數
+- Dataset: HuggingFaceVLA/libero
+- Seed: 1000
+- Steps: 1000
+- Batch size: 32
+- Hardware: MSI RTX 5090 Laptop 24GB
+
+## 狀態
+- 廢棄 (gradient conflict)
+- N/A
+
+## 相關
+- GitHub: KunHsiang/lerobot-vla-fbagent
+- W&B: N/A