---
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.**

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</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}")