--- 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