| ---
|
| license: mit
|
| task_categories:
|
| - robotics
|
| tags:
|
| - LeRobot
|
| - SO-101
|
| - imitation-learning
|
| - pick-and-place
|
| - SmolVLA
|
| - ACT
|
| - Diffusion
|
| - foundation-models
|
| ---
|
|
|
| # Armed Picky - Pin Sorting Dataset
|
|
|
| A LeRobot dataset for training a SO-101 robotic arm to sort colored pins into corresponding targets.
|
|
|
| ## Task Description
|
|
|
| **Objective:** Sort 2 colored pins into their matching targets
|
|
|
| | Pin Color | Target |
|
| |-----------|--------|
|
| | Blue | Circle (tape) |
|
| | Green | Square (box) |
|
|
|
| **Language instruction:** "Pick up the blue pin and place it in the circle. Pick up the green pin and place it in the square."
|
|
|
| ## Dataset Details
|
|
|
| | Property | Value |
|
| |----------|-------|
|
| | Robot | SO-101 Follower |
|
| | Episodes | 50 |
|
| | Total Frames | 67,085 |
|
| | FPS | 30 |
|
| | Cameras | 2 (front + side) |
|
| | Resolution | 640x480 |
|
| | Video Codec | AV1 |
|
|
|
| ## Data Structure
|
|
|
| **Actions (6 DOF):**
|
| - shoulder_pan, shoulder_lift, elbow_flex
|
| - wrist_flex, wrist_roll, gripper
|
|
|
| **Observations:**
|
| - `observation.state` - 6 joint positions
|
| - `observation.images.front` - front camera (640x480)
|
| - `observation.images.side` - side camera (640x480)
|
|
|
| ---
|
|
|
| ## 🤖 Trained Models (14 Total)
|
|
|
| We provide 14 trained models comparing 3 policies (ACT, Diffusion, Smol VLA) with different training configurations:
|
| - **From Scratch**: Random initialization
|
| - **Foundation Models**: Pre-trained vision encoders or full models
|
|
|
| ### ACT Models (2)
|
|
|
| | Config | Steps | Model Repository | Foundation |
|
| |--------|-------|-----------------|------------|
|
| | Scratch | 60K | `epochdev/act-scratch-60k` | - |
|
| | ViT | 60K | `epochdev/act-vit-60k` | ImageNet ViT |
|
|
|
| ### Diffusion Models (6)
|
|
|
| | Config | Steps | Model Repository | Foundation |
|
| |--------|-------|-----------------|------------|
|
| | Scratch | 20K | `epochdev/diffusion-scratch-20k` | - |
|
| | Scratch | 40K | `epochdev/diffusion-scratch-40k` | - |
|
| | Scratch | 60K | `epochdev/diffusion-scratch-60k` | - |
|
| | R3M | 20K | `epochdev/diffusion-r3m-20k` | R3M Vision Encoder |
|
| | R3M | 40K | `epochdev/diffusion-r3m-40k` | R3M Vision Encoder |
|
| | R3M | 60K | `epochdev/diffusion-r3m-60k` | R3M Vision Encoder |
|
|
|
| ### Smol VLA Models (6)
|
|
|
| | Config | Steps | Model Repository | Foundation |
|
| |--------|-------|-----------------|------------|
|
| | Scratch | 20K | `epochdev/smolvla-scratch-20k` | - |
|
| | Scratch | 40K | `epochdev/smolvla-scratch-40k` | - |
|
| | Scratch | 60K | `epochdev/smolvla-scratch-60k` | - |
|
| | OpenVLA | 20K | `epochdev/smolvla-openvla-20k` | OpenVLA-7B |
|
| | OpenVLA | 40K | `epochdev/smolvla-openvla-40k` | OpenVLA-7B |
|
| | OpenVLA | 60K | `epochdev/smolvla-openvla-60k` | OpenVLA-7B |
|
|
|
| ---
|
|
|
| ## 📚 Usage
|
|
|
| ### Quick Start - Loading the Dataset
|
|
|
| ```python
|
| from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
|
|
|
| dataset = LeRobotDataset("chvainickas/armed-picky-cleaned")
|
| ```
|
|
|
| ### Setup
|
|
|
| ```bash
|
| # Install LeRobot
|
| pip install -q lerobot
|
|
|
| # Login to HuggingFace
|
| from huggingface_hub import login
|
| login() # Enter your token
|
| ```
|
|
|
| ### Convert Dataset (First Time Only)
|
|
|
| ```bash
|
| # Convert to LeRobot v3.0 format
|
| rm -rf ~/.cache/huggingface/lerobot/chvainickas/armed-picky-cleaned
|
| python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id=chvainickas/armed-picky-cleaned
|
| ```
|
|
|
| ---
|
|
|
| ## 🚀 Training Models
|
|
|
| ### ACT Policy
|
|
|
| **From Scratch (60K steps)**
|
| ```bash
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=act \
|
| --steps=60000 \
|
| --output_dir=outputs/act-scratch-60k \
|
| --policy.repo_id=epochdev/act-scratch-60k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.type=adamw \
|
| --optimizer.lr=1e-5 \
|
| --optimizer.weight_decay=0.0001
|
| ```
|
|
|
| **With ImageNet ViT Foundation (60K steps)**
|
| ```bash
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=act \
|
| --policy.use_pretrained_vision=true \
|
| --policy.pretrained_backbone_weights=google/vit-base-patch16-224 \
|
| --steps=60000 \
|
| --output_dir=outputs/act-vit-60k \
|
| --policy.repo_id=epochdev/act-vit-60k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.type=adamw \
|
| --optimizer.lr=1e-5
|
| ```
|
|
|
| ### Diffusion Policy (Incremental Training)
|
|
|
| **From Scratch: 0→20K→40K→60K**
|
| ```bash
|
| # Step 1: Train 0→20K
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=diffusion \
|
| --steps=20000 \
|
| --output_dir=outputs/diffusion-scratch-20k \
|
| --policy.repo_id=epochdev/diffusion-scratch-20k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.lr=1e-4
|
|
|
| # Step 2: Resume 20K→40K
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=diffusion \
|
| --steps=40000 \
|
| --output_dir=outputs/diffusion-scratch-40k \
|
| --policy.repo_id=epochdev/diffusion-scratch-40k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.lr=1e-4 \
|
| --resume=true \
|
| --pretrained_policy_path=outputs/diffusion-scratch-20k/checkpoints/last/pretrained_model
|
|
|
| # Step 3: Resume 40K→60K
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=diffusion \
|
| --steps=60000 \
|
| --output_dir=outputs/diffusion-scratch-60k \
|
| --policy.repo_id=epochdev/diffusion-scratch-60k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.lr=1e-4 \
|
| --resume=true \
|
| --pretrained_policy_path=outputs/diffusion-scratch-40k/checkpoints/last/pretrained_model
|
| ```
|
|
|
| **With R3M Foundation: 0→20K→40K→60K**
|
| ```bash
|
| # Step 1: Train 0→20K with R3M
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=diffusion \
|
| --policy.use_pretrained_vision=true \
|
| --policy.pretrained_backbone=r3m \
|
| --steps=20000 \
|
| --output_dir=outputs/diffusion-r3m-20k \
|
| --policy.repo_id=epochdev/diffusion-r3m-20k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.lr=5e-5
|
|
|
| # Step 2: Resume 20K→40K
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=diffusion \
|
| --steps=40000 \
|
| --output_dir=outputs/diffusion-r3m-40k \
|
| --policy.repo_id=epochdev/diffusion-r3m-40k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.lr=5e-5 \
|
| --resume=true \
|
| --pretrained_policy_path=outputs/diffusion-r3m-20k/checkpoints/last/pretrained_model
|
|
|
| # Step 3: Resume 40K→60K
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=diffusion \
|
| --steps=60000 \
|
| --output_dir=outputs/diffusion-r3m-60k \
|
| --policy.repo_id=epochdev/diffusion-r3m-60k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.lr=5e-5 \
|
| --resume=true \
|
| --pretrained_policy_path=outputs/diffusion-r3m-40k/checkpoints/last/pretrained_model
|
| ```
|
|
|
| ### Smol VLA (Incremental Training)
|
|
|
| **From Scratch: 0→20K→40K→60K**
|
| ```bash
|
| # Step 1: Train 0→20K
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=vla \
|
| --steps=20000 \
|
| --output_dir=outputs/smolvla-scratch-20k \
|
| --policy.repo_id=epochdev/smolvla-scratch-20k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.lr=2e-5
|
|
|
| # Step 2: Resume 20K→40K
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=vla \
|
| --steps=40000 \
|
| --output_dir=outputs/smolvla-scratch-40k \
|
| --policy.repo_id=epochdev/smolvla-scratch-40k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.lr=2e-5 \
|
| --resume=true \
|
| --pretrained_policy_path=outputs/smolvla-scratch-20k/checkpoints/last/pretrained_model
|
|
|
| # Step 3: Resume 40K→60K
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=vla \
|
| --steps=60000 \
|
| --output_dir=outputs/smolvla-scratch-60k \
|
| --policy.repo_id=epochdev/smolvla-scratch-60k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.lr=2e-5 \
|
| --resume=true \
|
| --pretrained_policy_path=outputs/smolvla-scratch-40k/checkpoints/last/pretrained_model
|
| ```
|
|
|
| **Fine-tuned from OpenVLA: 0→20K→40K→60K**
|
| ```bash
|
| # Step 1: Fine-tune 0→20K from OpenVLA
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=vla \
|
| --policy.pretrained_model=openvla/openvla-7b \
|
| --steps=20000 \
|
| --output_dir=outputs/smolvla-openvla-20k \
|
| --policy.repo_id=epochdev/smolvla-openvla-20k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.lr=5e-6
|
|
|
| # Step 2: Continue 20K→40K
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=vla \
|
| --steps=40000 \
|
| --output_dir=outputs/smolvla-openvla-40k \
|
| --policy.repo_id=epochdev/smolvla-openvla-40k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.lr=5e-6 \
|
| --resume=true \
|
| --pretrained_policy_path=outputs/smolvla-openvla-20k/checkpoints/last/pretrained_model
|
|
|
| # Step 3: Continue 40K→60K
|
| lerobot-train \
|
| --dataset.repo_id=chvainickas/armed-picky-cleaned \
|
| --policy=vla \
|
| --steps=60000 \
|
| --output_dir=outputs/smolvla-openvla-60k \
|
| --policy.repo_id=epochdev/smolvla-openvla-60k \
|
| --policy.device=cuda \
|
| --wandb.enable=false \
|
| --optimizer.lr=5e-6 \
|
| --resume=true \
|
| --pretrained_policy_path=outputs/smolvla-openvla-40k/checkpoints/last/pretrained_model
|
| ```
|
|
|
| ---
|
|
|
| ## 📤 Upload Checkpoints
|
|
|
| ```python
|
| from huggingface_hub import HfApi
|
|
|
| api = HfApi()
|
| api.upload_folder(
|
| folder_path="outputs/act-scratch-60k/checkpoints/last/pretrained_model",
|
| repo_id="epochdev/act-scratch-60k",
|
| repo_type="model",
|
| )
|
| ```
|
|
|
| ---
|
|
|
| ## 🤖 Running on Robot
|
|
|
| ```bash
|
| lerobot-record \
|
| --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: 1, width: 640, height: 480, fps: 30}}" \
|
| --dataset.single_task="Pick up the blue pin and place it in the circle. Pick up the green pin and place it in the square." \
|
| --policy.path=epochdev/smolvla-openvla-60k
|
| ```
|
|
|
| ---
|
|
|
| ## 🔬 Model Comparison Study
|
|
|
| All 14 models are trained on the same dataset with the same 60K step budget to enable fair comparison:
|
|
|
| **Research Questions:**
|
| 1. Does using foundation models improve performance?
|
| 2. Which policy architecture works best for this task?
|
| 3. How does performance scale with training steps (20K vs 40K vs 60K)?
|
| 4. When do foundation models help most (early vs late training)?
|
|
|
| **Foundation Models Used:**
|
| - **ACT**: ImageNet pre-trained Vision Transformer (ViT)
|
| - **Diffusion**: R3M (robot-specific vision encoder)
|
| - **Smol VLA**: OpenVLA-7B (full VLA model trained on Open X-Embodiment)
|
|
|
| ---
|
|
|
| ## 💡 Training Tips
|
|
|
| **Google Colab:**
|
| - Use T4 GPU (free tier) or A100 (Colab Pro)
|
| - Save outputs to Google Drive to prevent data loss
|
| - Enable mixed precision with `--fp16=true` for faster training
|
|
|
| **Resume Training:**
|
| - All models support checkpoint resumption with `--resume=true`
|
| - Use `--pretrained_policy_path` to specify checkpoint location
|
| - Incremental training (20K→40K→60K) is more efficient than training from scratch
|
|
|
| **Experiment Tracking:**
|
| - Remove `--wandb.enable=false` and set wandb API key to track experiments
|
| - Monitor training curves to detect overfitting or convergence
|
|
|
| ---
|
|
|
| ## 📊 Origin
|
|
|
| Cleaned version of [Amirzon10/armed-picky](https://huggingface.co/datasets/Amirzon10/armed-picky) with 1 bad episode removed and indices renumbered.
|
|
|
| ## 📄 License
|
|
|
| MIT
|
|
|
