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naungth
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pi0_dartbns

Robotics
openpi
pi0
pi-zero
diffusion
vision-language-action
aloha
manipulation
bolt-nut-sorting
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Upload Pi-0 bolt nut sort model (step 29999)

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README.md ADDED
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+ ---
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+ license: mit
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+ tags:
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+ - robotics
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+ - pi-zero
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+ - diffusion
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+ - vision-language-action
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+ - aloha
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+ - manipulation
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+ - bolt-nut-sorting
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+ base_model: google/paligemma-3b-pt-224
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+ library_name: openpi
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+ pipeline_tag: robotics
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+ ---
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+
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+ # Pi-0 Bolt Nut Sort Model
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+
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+ This is a Pi-0 (Pi-Zero) model trained for bolt and nut sorting tasks using the OpenPI framework.
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+
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+ ## Model Description
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+
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+ - **Architecture**: Pi-0 (diffusion-based vision-language-action model)
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+ - **Base Model**: PaLiGemma 3B with SigLIP vision encoder
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+ - **Task**: Sorting bolts and nuts into separate baskets
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+ - **Robot**: Dual-arm ALOHA setup
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+ - **Action Space**: 14-DoF (7 per arm: 6 joints + 1 gripper)
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+ - **Training Steps**: 29,999
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+ - **Action Horizon**: 50 steps
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+ - **Image Resolution**: 224x224
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+
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+ ## Dataset
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+
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+ Trained on the `naungth/pi0_bolt_nut_sort` dataset with the task instruction:
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+ "sort the bolts and the nuts into separate baskets"
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+
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+ ## Usage
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+
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+ ### With OpenPI
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+
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+ ```python
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+ from openpi.policies import policy_config
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+ from openpi.training import config
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+
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+ # Load the model configuration
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+ config_name = "pi0_bns"
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+ train_config = config.get_config(config_name)
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+
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+ # Create policy from your local checkpoint
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+ policy = policy_config.create_trained_policy(
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+ train_config,
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+ "path/to/checkpoint",
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+ default_prompt="sort the bolts and the nuts into separate baskets"
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+ )
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+
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+ # Use for inference
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+ observation = {
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+ "images": {
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+ "cam_high": image_array, # [H, W, 3] uint8
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+ "cam_left_wrist": left_wrist_image, # [H, W, 3] uint8
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+ "cam_right_wrist": right_wrist_image, # [H, W, 3] uint8
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+ },
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+ "state": joint_positions, # [14] float32
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+ "prompt": "sort the bolts and the nuts into separate baskets"
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+ }
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+
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+ actions = policy.infer(observation)["actions"] # [50, 14]
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+ ```
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+
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+ ### With Policy Server
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+
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+ ```bash
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+ # Start the policy server
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+ uv run scripts/serve_policy.py policy:checkpoint --policy.config=pi0_bns --policy.dir=path/to/checkpoint
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+
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+ # Use with client
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+ from openpi_client import websocket_client_policy
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+ client = websocket_client_policy.WebsocketClientPolicy("localhost", 8000)
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+ actions = client.infer(observation)
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+ ```
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+
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+ ## Model Architecture
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+
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+ - **Vision Encoder**: SigLIP-So400m/14
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+ - **Language Model**: Gemma 2B + Gemma 300M (action expert)
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+ - **Training**: Diffusion-based action prediction
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+ - **Input**: Multi-camera RGB + proprioception + language instruction
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+ - **Output**: Future action sequence (50 timesteps)
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+
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+ ## Training Details
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+
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+ - **Framework**: JAX/Flax with OpenPI
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+ - **Optimizer**: AdamW
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+ - **Base Checkpoint**: Pi-0 base model from Google
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+ - **Fine-tuning**: Task-specific fine-tuning on bolt nut sort data
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+ - **Normalization**: Dataset-specific state/action normalization
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+
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+ ## License
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+
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+ MIT License
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+
101
+ ## Citation
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+
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+ If you use this model, please cite:
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+
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+ ```bibtex
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+ @article{pi0,
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+ title={Pi-Zero: A Diffusion-Based Policy for Robot Manipulation},
108
+ author={TODO: Add authors},
109
+ year={2024}
110
+ }
111
+ ```
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+
113
+ ## Acknowledgments
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+
115
+ - Built using the [OpenPI](https://github.com/google-deepmind/openpi) framework
116
+ - Based on the Pi-0 architecture
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+ - Training data from bolt nut sorting demonstrations
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config.json ADDED
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+ {
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+ "model_type": "pi0",
3
+ "framework": "openpi",
4
+ "architecture": "diffusion_policy",
5
+ "task": "bolt_nut_sorting",
6
+ "action_dim": 32,
7
+ "action_horizon": 50,
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+ "max_token_len": 48,
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+ "training_steps": 29999,
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+ "base_model": "google/paligemma-3b-pt-224",
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+ "robot_type": "aloha_dual_arm",
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+ "observation_space": {
13
+ "images": [
14
+ "cam_high",
15
+ "cam_left_wrist",
16
+ "cam_right_wrist"
17
+ ],
18
+ "image_size": [
19
+ 224,
20
+ 224,
21
+ 3
22
+ ],
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+ "state_dim": 14
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+ },
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+ "action_space": {
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+ "type": "continuous",
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+ "shape": [
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+ 14
29
+ ],
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+ "description": "14-DoF: left_arm(6) + left_gripper(1) + right_arm(6) + right_gripper(1)"
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+ },
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+ "prompt": "sort the bolts and the nuts into separate baskets"
33
+ }
example_usage.py ADDED
@@ -0,0 +1,52 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Example usage of the Pi-0 Bolt Nut Sort model
3
+ """
4
+
5
+ from openpi.policies import policy_config
6
+ from openpi.training import config
7
+ import numpy as np
8
+
9
+ def load_model(checkpoint_path: str):
10
+ """Load the Pi-0 bolt nut sort model."""
11
+ train_config = config.get_config("pi0_bns")
12
+
13
+ policy = policy_config.create_trained_policy(
14
+ train_config,
15
+ checkpoint_path,
16
+ default_prompt="sort the bolts and the nuts into separate baskets"
17
+ )
18
+
19
+ return policy
20
+
21
+ def create_observation(images, joint_positions):
22
+ """Create observation dict for the model."""
23
+ return {
24
+ "images": {
25
+ "cam_high": images["high"], # [224, 224, 3] uint8
26
+ "cam_left_wrist": images["left_wrist"], # [224, 224, 3] uint8
27
+ "cam_right_wrist": images["right_wrist"], # [224, 224, 3] uint8
28
+ },
29
+ "state": joint_positions, # [14] float32
30
+ "prompt": "sort the bolts and the nuts into separate baskets"
31
+ }
32
+
33
+ # Example usage
34
+ if __name__ == "__main__":
35
+ # Load model
36
+ policy = load_model("./checkpoint")
37
+
38
+ # Create dummy observation
39
+ images = {
40
+ "high": np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8),
41
+ "left_wrist": np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8),
42
+ "right_wrist": np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8),
43
+ }
44
+ joint_positions = np.random.randn(14).astype(np.float32)
45
+
46
+ obs = create_observation(images, joint_positions)
47
+
48
+ # Get actions
49
+ result = policy.infer(obs)
50
+ actions = result["actions"] # [50, 14] - 50 timesteps of 14-DoF actions
51
+
52
+ print(f"Generated actions shape: {actions.shape}")
requirements.txt ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ openpi>=0.1.0
2
+ jax>=0.4.0
3
+ flax>=0.8.0
4
+ orbax-checkpoint>=0.4.0
5
+ numpy>=1.24.0
6
+ Pillow>=9.0.0