Upload Pi-0 bolt nut sort model (step 29999)

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README.md

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+---
+license: mit
+tags:
+- robotics
+- pi-zero
+- diffusion
+- vision-language-action
+- aloha
+- manipulation
+- bolt-nut-sorting
+base_model: google/paligemma-3b-pt-224
+library_name: openpi
+pipeline_tag: robotics
+---
+
+# Pi-0 Bolt Nut Sort Model
+
+This is a Pi-0 (Pi-Zero) model trained for bolt and nut sorting tasks using the OpenPI framework.
+
+## Model Description
+
+- **Architecture**: Pi-0 (diffusion-based vision-language-action model)
+- **Base Model**: PaLiGemma 3B with SigLIP vision encoder
+- **Task**: Sorting bolts and nuts into separate baskets
+- **Robot**: Dual-arm ALOHA setup
+- **Action Space**: 14-DoF (7 per arm: 6 joints + 1 gripper)
+- **Training Steps**: 29,999
+- **Action Horizon**: 50 steps
+- **Image Resolution**: 224x224
+
+## Dataset
+
+Trained on the `naungth/pi0_bolt_nut_sort` dataset with the task instruction:
+"sort the bolts and the nuts into separate baskets"
+
+## Usage
+
+### With OpenPI
+
+```python
+from openpi.policies import policy_config
+from openpi.training import config
+
+# Load the model configuration
+config_name = "pi0_bns" 
+train_config = config.get_config(config_name)
+
+# Create policy from your local checkpoint
+policy = policy_config.create_trained_policy(
+    train_config, 
+    "path/to/checkpoint",
+    default_prompt="sort the bolts and the nuts into separate baskets"
+)
+
+# Use for inference
+observation = {
+    "images": {
+        "cam_high": image_array,  # [H, W, 3] uint8
+        "cam_left_wrist": left_wrist_image,  # [H, W, 3] uint8  
+        "cam_right_wrist": right_wrist_image,  # [H, W, 3] uint8
+    },
+    "state": joint_positions,  # [14] float32
+    "prompt": "sort the bolts and the nuts into separate baskets"
+}
+
+actions = policy.infer(observation)["actions"]  # [50, 14]
+```
+
+### With Policy Server
+
+```bash
+# Start the policy server
+uv run scripts/serve_policy.py policy:checkpoint \
+    --policy.config=pi0_bns \
+    --policy.dir=path/to/checkpoint
+
+# Use with client
+from openpi_client import websocket_client_policy
+client = websocket_client_policy.WebsocketClientPolicy("localhost", 8000)
+actions = client.infer(observation)
+```
+
+## Model Architecture
+
+- **Vision Encoder**: SigLIP-So400m/14 
+- **Language Model**: Gemma 2B + Gemma 300M (action expert)
+- **Training**: Diffusion-based action prediction
+- **Input**: Multi-camera RGB + proprioception + language instruction
+- **Output**: Future action sequence (50 timesteps)
+
+## Training Details
+
+- **Framework**: JAX/Flax with OpenPI
+- **Optimizer**: AdamW
+- **Base Checkpoint**: Pi-0 base model from Google
+- **Fine-tuning**: Task-specific fine-tuning on bolt nut sort data
+- **Normalization**: Dataset-specific state/action normalization
+
+## License
+
+MIT License
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@article{pi0,
+  title={Pi-Zero: A Diffusion-Based Policy for Robot Manipulation},
+  author={TODO: Add authors},
+  year={2024}
+}
+```
+
+## Acknowledgments
+
+- Built using the [OpenPI](https://github.com/google-deepmind/openpi) framework
+- Based on the Pi-0 architecture
+- Training data from bolt nut sorting demonstrations

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checkpoint/params/_sharding

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checkpoint/train_state/_sharding

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+  "architecture": "diffusion_policy",
+  "task": "bolt_nut_sorting",
+  "action_dim": 32,
+  "action_horizon": 50,
+  "max_token_len": 48,
+  "training_steps": 29999,
+  "base_model": "google/paligemma-3b-pt-224",
+  "robot_type": "aloha_dual_arm",
+  "observation_space": {
+    "images": [
+      "cam_high",
+      "cam_left_wrist",
+      "cam_right_wrist"
+    ],
+    "image_size": [
+      224,
+      224,
+      3
+    ],
+    "state_dim": 14
+  },
+  "action_space": {
+    "type": "continuous",
+    "shape": [
+      14
+    ],
+    "description": "14-DoF: left_arm(6) + left_gripper(1) + right_arm(6) + right_gripper(1)"
+  },
+  "prompt": "sort the bolts and the nuts into separate baskets"
+}

example_usage.py

@@ -0,0 +1,52 @@
+"""
+Example usage of the Pi-0 Bolt Nut Sort model
+"""
+
+from openpi.policies import policy_config
+from openpi.training import config
+import numpy as np
+
+def load_model(checkpoint_path: str):
+    """Load the Pi-0 bolt nut sort model."""
+    train_config = config.get_config("pi0_bns")
+    
+    policy = policy_config.create_trained_policy(
+        train_config,
+        checkpoint_path,
+        default_prompt="sort the bolts and the nuts into separate baskets"
+    )
+    
+    return policy
+
+def create_observation(images, joint_positions):
+    """Create observation dict for the model."""
+    return {
+        "images": {
+            "cam_high": images["high"],  # [224, 224, 3] uint8
+            "cam_left_wrist": images["left_wrist"],  # [224, 224, 3] uint8  
+            "cam_right_wrist": images["right_wrist"],  # [224, 224, 3] uint8
+        },
+        "state": joint_positions,  # [14] float32 
+        "prompt": "sort the bolts and the nuts into separate baskets"
+    }
+
+# Example usage
+if __name__ == "__main__":
+    # Load model
+    policy = load_model("./checkpoint")
+    
+    # Create dummy observation
+    images = {
+        "high": np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8),
+        "left_wrist": np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8),
+        "right_wrist": np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8),
+    }
+    joint_positions = np.random.randn(14).astype(np.float32)
+    
+    obs = create_observation(images, joint_positions)
+    
+    # Get actions
+    result = policy.infer(obs)
+    actions = result["actions"]  # [50, 14] - 50 timesteps of 14-DoF actions
+    
+    print(f"Generated actions shape: {actions.shape}")

requirements.txt

@@ -0,0 +1,6 @@
+openpi>=0.1.0
+jax>=0.4.0
+flax>=0.8.0
+orbax-checkpoint>=0.4.0
+numpy>=1.24.0
+Pillow>=9.0.0