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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">
<video src="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" width="400" controls></video>
</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}")
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