metadata
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.
📖 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.
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}")