Create README.md

README.md

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+---
+license: mit
+task_categories:
+- robotics
+- reinforcement-learning
+tags:
+- act
+- imitation-learning
+- mujoco
+- simulation
+- aloha
+size_categories:
+- n<1K
+---
+
+# 🦾 ACT Simulation Dataset (v2): Cube Sort
+
+**A lightweight, randomized imitation learning dataset optimized for Action Chunking Transformers.**
+
+<div align="center">
+![Cube Sort Training Video](https://github-production-user-asset-6210df.s3.amazonaws.com/71585678/520477863-ec1d9885-0d81-46a1-a29d-c0ef0e782fff.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20251201%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20251201T004617Z&X-Amz-Expires=300&X-Amz-Signature=dd18d7f406bcc24fbd9ddde59686ea3984949a407477cf45acfd2e2f9a709370&X-Amz-SignedHeaders=host)
+</div>
+
+
+## 📖 Overview
+This dataset contains **228 episodes** of synthetic robot manipulation data generated using MuJoCo. It was created to train **Action Chunking Transformers (ACT)** on consumer hardware (specifically an Apple M2 MacBook Air).
+
+Unlike "sterile" datasets that follow perfect straight lines, this **v2 dataset** utilizes **Domain Randomization** to ensure the policy learns robust correction behaviors rather than simple memorization.
+
+- **Task:** A 4-DOF Robotic Arm picking up a cube and moving it to a specific target zone.
+- **Hardware Virtualization:** Mimics the data structure of a real Dynamixel-based robot arm.
+- **Goal:** Enable "Zero-to-Hero" training of Visuomotor Policies without NVIDIA H100s.
+
+## ⚙️ Dataset Specifications
+
+| Feature | Details |
+| :--- | :--- |
+| **Episodes** | 228 (approx. 68,000 timesteps) |
+| **Format** | `.hdf5` |
+| **Simulation** | MuJoCo (via `dm_control` / `mujoco-py`) |
+| **Robot** | Custom 4-DOF Arm (Base, Shoulder, Elbow, Wrist) |
+| **Camera** | 1x Static Front Camera (480x640, RGB) |
+| **Control Freq** | 50Hz |
+
+### 🧠 Data Structure (HDF5)
+Each `.hdf5` file represents one full episode and contains:
+
+- `observations/images/front`: `(T, 480, 640, 3)` - Raw RGB images.
+- `observations/qpos`: `(T, 4)` - Joint positions (radians).
+- `observations/qvel`: `(T, 4)` - Joint velocities.
+- `action`: `(T, 4)` - Target joint positions for the next step.
+
+## 🛠️ How to Use
+
+### 1. Download & Unzip
+This dataset is stored as a compressed ZIP file (`episodes-v2.zip`) to maintain directory structure.
+
+```python
+from huggingface_hub import hf_hub_download
+import zipfile
+import os
+
+# 1. Download
+zip_path = hf_hub_download(
+    repo_id="sanskxr02/act_sim_cube_sort",
+    filename="episodes-v2.zip",
+    repo_type="dataset"
+)
+
+# 2. Extract
+extract_path = "data/sim_cube_sort"
+os.makedirs(extract_path, exist_ok=True)
+
+with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+    zip_ref.extractall(extract_path)
+
+print(f"✅ Dataset ready at {extract_path}")
+
+
+
+
+