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CYBER Memory Model

The development of world models in robotics has long been a cornerstone of advanced research, with most approaches relying heavily on vast, platform-specific datasets. These datasets, while valuable, often limit scalability and generalization to different robotic platforms, restricting their broader applicability.

In contrast, CYBER approaches world modeling from a "first principles" perspective, drawing inspiration from how humans naturally acquire skills through experience and interaction with their environment. Unlike existing robotic world models, CYBER is the first real-world operational system designed to adapt to diverse and challenging environments. It merges a Physical World Model with a Visual-Language Model (VLM) to create a groundbreaking, holistic framework that enables robots to learn, predict, and perform across various tasks and embodiments.

We can also construct a more complex model by combining the world model, action model, perception model, memory model, and control model to build a more complex model for a specific task. Here is an example code snippet:

# Example usage of CYBER combined models for a complex task
from cyber.models.world import WorldModel
from cyber.models.action import ActionModel
from cyber.models.perception import PerceptionModel
from cyber.models.memory import MemoryModel
from cyber.models.control import ControlModel

# Initialize all models
world_model = WorldModel()
action_model = ActionModel()
perception_model = PerceptionModel()
memory_model = MemoryModel()
control_model = ControlModel()

# Load pre-trained weights if available
world_model.load_weights('path/to/world_model_weights')
action_model.load_weights('path/to/action_model_weights')
perception_model.load_weights('path/to/perception_model_weights')
memory_model.load_weights('path/to/memory_model_weights')
control_model.load_weights('path/to/control_model_weights')

# Example input data for the complex task
input_data = {
        'sensor_data': [0.1, 0.2, 0.3],
        'action_data': [1, 0, 1],
        'task_specific_data': [0.5, 0.6, 0.7],
        'additional_context': [1, 1, 0]
}

# Perceive the environment
perceived_data = perception_model.process(input_data['sensor_data'])

# Utilize memory for context
contextual_data = memory_model.retrieve(input_data['additional_context'])

# Predict the next state using the world model
predicted_state = world_model.predict({
        'perceived_data': perceived_data,
        'contextual_data': contextual_data,
        'task_specific_data': input_data['task_specific_data']
})

# Predict the next action using the action model
predicted_action = action_model.predict({
        'current_state': predicted_state,
        'goal_state': [1, 0, 1]
})

# Control the robot using the control model
control_signals = control_model.generate(predicted_action)

print(f"Predicted State: {predicted_state}")
print(f"Predicted Action: {predicted_action}")
print(f"Control Signals: {control_signals}")