Update README.md

README.md

@@ -1,62 +1,44 @@
 ---
-datasets: AdrielP/cables_il_only_black
-library_name: lerobot
 license: apache-2.0
-model_name: act
-pipeline_tag: robotics
+library_name: lerobot
 tags:
 - robotics
-- lerobot
 - act
+- imitation-learning
+- so101
+- cable-sorting
+datasets:
+- AdrielP/cables_il_only_black
+pipeline_tag: robotics
 ---
 
-# Model Card for act
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
-
-
-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
+# ACT - Cable Sorting (Only Black)
 
-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:
+Modelo de Imitation Learning entrenado con Action Chunking Transformers (ACT) para ordenamiento de cables con el robot SO101.
 
-### Train from scratch
+## Tarea
+Cuando **solo el cable negro** está visible → el robot quita el cable amarillo y lo coloca en la caja amarilla.
 
-```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
-```
+## Detalles de entrenamiento
+- **Algoritmo:** Behaviour Cloning con ACT
+- **Dataset:** AdrielP/cables_il_only_black
+- **Episodios:** 100 demostraciones
+- **Steps:** 50,000
+- **Batch size:** 8
+- **Robot:** SO101 Follower
+- **Cámaras:** front (640x480) + side (640x480) a 30fps
 
-_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
-
-### Evaluate the policy/run inference
+## Cómo usar
 
 ```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
+  --robot.type=so101_follower \
+  --robot.port=/dev/ttyACM0 \
+  --robot.cameras='{"front":{"type":"opencv","index_or_path":0,"width":640,"height":480,"fps":30},"side":{"type":"opencv","index_or_path":2,"width":640,"height":480,"fps":30}}' \
+  --policy.path=AdrielP/act_only_black \
+  --dataset.repo_id=AdrielP/eval_only_black \
+  --dataset.single_task="state based cable sorting" \
+  --dataset.num_episodes=5 \
+  --dataset.fps=15 \
+  --play_sounds=false
+```