robots.py

import numpy as np
import cv2
import tensorflow as tf
from tensorflow.keras.layers import LSTM, Dense, Input, TimeDistributed, Attention, Concatenate, Flatten, Reshape, RepeatVector, MultiHeadAttention, LayerNormalization, Dropout
from tensorflow.keras.models import Model
import networkx as nx
import torch
import torch_geometric
from torch_geometric.data import Data
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import LaserScan
import google.cloud.pubsub_v1 as pubsub
import json
from google.cloud import aiplatform
from datetime import datetime, timedelta
from google.cloud import storage

# Object Detection Model (e.g., YOLO)
object_detector = cv2.dnn.readNetFromDarknet("yolov3.cfg", "yolov3.weights")

# Kalman Filter 
class KalmanTracker:
    def __init__(self, initial_state):
        self.filter = cv2.KalmanFilter(4, 2) 
        self.filter.measurementMatrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], np.float32) 
        self.filter.transitionMatrix = np.array([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) 
        self.filter.processNoiseCov = np.eye(4, dtype=np.float32) * 0.03 
        self.filter.measurementNoiseCov = np.eye(2, dtype=np.float32) * 0.1
        self.filter.statePost = initial_state

    def predict(self):
        predicted_state = self.filter.predict()
        return predicted_state

    def update(self, measurement):
        self.filter.correct(measurement)
        return self.filter.statePost

# Transformer Block
class TransformerBlock(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads, dff, rate=0.1):
        super(TransformerBlock, self).__init__()
        self.mha = MultiHeadAttention(num_heads=num_heads, key_dim=d_model)
        self.layernorm1 = LayerNormalization(epsilon=1e-6)
        self.ffn = tf.keras.Sequential(
            [Dense(dff, activation='relu'), Dense(d_model)]
        )
        self.layernorm2 = LayerNormalization(epsilon=1e-6)
        self.dropout1 = tf.keras.layers.Dropout(rate)
        self.dropout2 = tf.keras.layers.Dropout(rate)

    def call(self, x, training):
        attn_output, attn_weights = self.mha(x, x, x)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(x + attn_output)

        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = self.layernorm2(out1 + ffn_output)
        return out2

# Neural Network for Learning Object Behavior (Transformer)
class ObjectBehaviorLearner:
    def __init__(self, prediction_horizon=10, state_dimension=4, d_model=128, num_heads=8, dff=512, num_layers=2):
        self.prediction_horizon = prediction_horizon
        self.state_dimension = state_dimension
        self.object_graph = nx.Graph()  # Initialize an empty graph

        # Define the Transformer architecture
        input_layer = Input(shape=(None, self.state_dimension))  # Input shape: (time steps, state dimensions)

        # Embedding Layer
        x = Dense(d_model)(input_layer)

        # Transformer Blocks
        for _ in range(num_layers):
            x = TransformerBlock(d_model, num_heads, dff)(x)

        # Output Layer
        output_layer = TimeDistributed(Dense(self.state_dimension))(x)

        # Create the model
        self.model = Model(inputs=input_layer, outputs=output_layer)
        self.model.compile(optimizer='adam', loss='mse')

    def train(self, states, future_states):
        # Train the Transformer on object states and future predictions
        self.model.fit(states, future_states, epochs=10, batch_size=32, validation_split=0.2)

    def predict(self, states):
        """Predicts future states based on the learned behavior."""
        predictions = self.model.predict(states)
        return predictions

    def update_graph(self, object_id, state, predicted_state, alpha=0.5):
        """Updates the object graph with new information."""
        now = datetime.utcnow()  # Get current time 
        if object_id not in self.object_graph.nodes:
            self.object_graph.add_node(object_id, 
                                      state=state,  # Add the object's state
                                      label=None, 
                                      description=None,
                                      functionality=None,
                                      affordances=None,
                                      gemini_confidence=None,
                                      explored=False,
                                      timestamp=now.isoformat()) 
        else:
            self.object_graph.nodes[object_id]['state'] = state
            self.object_graph.nodes[object_id]['explored'] = True
            self.object_graph.nodes[object_id]['timestamp'] = now.isoformat() # Update timestamp

        # Calculate velocity difference
        velocity_diff = predicted_state[2:] - state[2:]

        # Add an edge with velocity-based weight
        self.object_graph.add_edge(
            object_id, object_id,
            weight=np.linalg.norm(state - predicted_state) + alpha * np.linalg.norm(velocity_diff)
        )

    def get_graph_information(self):
        """Retrieves information about the learned object graph."""
        # You can use NetworkX functions to access graph information
        # e.g., self.object_graph.degree(object_id) for the degree of a node
        #       self.object_graph.edges(object_id) for edges connected to a node
        pass

    def get_unexplored_objects(self):
        """Returns a list of unexplored object IDs."""
        return [node for node, data in self.object_graph.nodes(data=True) if not data.get('explored', False)]

    def publish_object_info(self, node_id):
        """Publishes information about unexplored objects."""
        unexplored_objects = self.get_unexplored_objects()
        if unexplored_objects:
            message = {
                'node_id': node_id,
                'unexplored_objects': unexplored_objects
            }
            data = json.dumps(message).encode('utf-8')
            future = publisher.publish(topic_path, data)
            print(f"Published object info message ID: {future.result()}")

    def request_object_info(self, node_id):
        """Publishes a request for other nodes to share object info."""
        message = {
            'node_id': node_id,
            'requesting_object_info': True
        }
        data = json.dumps(message).encode('utf-8')
        future = publisher.publish(topic_path, data)
        print(f"Published object info request message ID: {future.result()}")

# Graph Neural Network Model
class ObjectBehaviorGNN(torch.nn.Module):
    def __init__(self, hidden_channels, state_dimension=4):
        super().__init__()
        self.state_dimension = state_dimension
        self.conv1 = torch_geometric.nn.GCNConv(self.state_dimension, hidden_channels)
        self.conv2 = torch_geometric.nn.GCNConv(hidden_channels, self.state_dimension)

    def forward(self, x, edge_index):
        x = self.conv1(x, edge_index)
        x = torch.relu(x)
        x = self.conv2(x, edge_index)
        return x

# Neural Network for Combining Predictions
class PredictionCombiner(tf.keras.Model):
    def __init__(self, input_dim, output_dim, prediction_horizon=10, use_confidence_weighting=True, dropout_rate=0.2):
        super(PredictionCombiner, self).__init__()
        self.use_confidence_weighting = use_confidence_weighting
        self.dropout_rate = dropout_rate
        self.prediction_horizon = prediction_horizon

        self.dense1 = Dense(128, activation='relu')
        self.dense2 = Dense(64, activation='relu')
        self.dense3 = Dense(output_dim * self.prediction_horizon)  # Output waypoints for the prediction horizon
        self.layer_norm = LayerNormalization(epsilon=1e-6)
        self.dropout = Dropout(self.dropout_rate)

    def call(self, inputs):
        # Inputs: [rnn_predictions, gnn_predictions, rnn_confidences, gnn_confidences, ... contextual information]
        rnn_predictions, gnn_predictions, rnn_confidences, gnn_confidences, environment_info, robot_state, obstacles = inputs

        # Flatten only if necessary
        if len(rnn_predictions.shape) > 2:
            rnn_predictions = Flatten()(rnn_predictions)
        if len(gnn_predictions.shape) > 2:
            gnn_predictions = Flatten()(gnn_predictions)
        if len(rnn_confidences.shape) > 1:
            rnn_confidences = Flatten()(rnn_confidences)
        if len(gnn_confidences.shape) > 1:
            gnn_confidences = Flatten()(gnn_confidences)
        if len(environment_info.shape) > 2:
            environment_info = Flatten()(environment_info)
        if len(robot_state.shape) > 2:
            robot_state = Flatten()(robot_state)
        if len(obstacles.shape) > 1:
            obstacles = Flatten()(obstacles)

        # Confidence Weighting
        if self.use_confidence_weighting:
            rnn_predictions = rnn_predictions * rnn_confidences[:, tf.newaxis]
            gnn_predictions = gnn_predictions * gnn_confidences[:, tf.newaxis]

        # Concatenate and Normalize
        combined_input = Concatenate()([rnn_predictions, gnn_predictions, environment_info, robot_state, obstacles])
        combined_input = self.layer_norm(combined_input)
        combined_input = self.dropout(combined_input, training=True)

        # Dense Layers
        combined_input = self.dense1(combined_input)
        combined_input = self.dense2(combined_input)
        waypoints = self.dense3(combined_input)
        waypoints = Reshape((self.prediction_horizon, output_dim))(waypoints)  # Reshape into waypoints
        return waypoints

# Global variable to store environment information
environment_info = None

class EnvironmentInfoNode(Node):
    def __init__(self):
        super().__init__('environment_info_node')
        self.create_subscription(LaserScan, '/scan', self.lidar_callback, 10)

    def lidar_callback(self, msg):
        global environment_info  # Access the global variable
        # Process the Lidar scan data
        ranges = np.array(msg.ranges)
        # ... (Implement your logic to detect obstacles and extract information)

        # Example: Return distances to obstacles as environment information
        environment_info = ranges  # Replace with your actual environment information 

# Pub/Sub setup 
publisher = pubsub.PublisherClient()
subscriber = pubsub.SubscriberClient()
project_id = 'your-gcp-project-id'
topic_id = 'object_exploration'  # Use a descriptive topic ID
subscription_id = 'robot_subscription'
topic_path = publisher.topic_path(project_id, topic_id)
subscription_path = subscriber.subscription_path(project_id, subscription_id)

# Create the topic and subscription (if they don't exist)
try:
    publisher.create_topic(name=topic_path)
except: 
    pass

try:
    subscriber.create_subscription(name=subscription_path, topic=topic_path)
except:
    pass

# Function to Save Graph to Cloud Storage
def save_graph_to_cloud_storage(graph, bucket_name, blob_name):
    """Saves the object graph to Google Cloud Storage."""
    storage_client = storage.Client()
    bucket = storage_client.bucket(bucket_name)
    blob = bucket.blob(blob_name)
    graph_data = nx.node_link_data(graph)
    blob.upload_from_string(json.dumps(graph_data))
    print(f"Graph data saved to gs://{bucket_name}/{blob_name}")

# Robot Loop
def robot_loop():
    learner = ObjectBehaviorLearner(state_dimension=4)  # Initialize the learner with a graph
    gnn_model = ObjectBehaviorGNN(hidden_channels=64, state_dimension=4) 
    optimizer_gnn = torch.optim.Adam(gnn_model.parameters())
    combiner = PredictionCombiner(input_dim=state_dimension * 2 + 3 + 3, output_dim=state_dimension, prediction_horizon=10)  # Input is RNN + GNN predictions + environment_info + robot_state
    optimizer_combiner = tf.keras.optimizers.Adam()

    # Pre-train the Combiner (if you have a dataset)
    pre_training_data_available = True  # Set to True if you have pre-training data
    if pre_training_data_available:
        rnn_predictions_train, gnn_predictions_train, rnn_confidences_train, gnn_confidences_train, environment_info_train, robot_state_train, actual_states_train = load_pre_training_data()  # Implement this function

        combiner.compile(loss=custom_loss, optimizer=optimizer_combiner)
        combiner.fit(
            [rnn_predictions_train, gnn_predictions_train, rnn_confidences_train, gnn_confidences_train, environment_info_train, robot_state_train],
            actual_states_train,
            epochs=10,  # Adjust as needed
            verbose=0
        )

    # Initialize the ROS node
    rclpy.init(args=None)
    environment_info_node = EnvironmentInfoNode()

    robot_id = 'robot_1'  # Assign a unique ID to this robot
    frame_count = 0

    while True:
        # Capture a frame from the robot's camera
        frame = capture.read()
        frame_count += 1

        # Object Detection
        detections = object_detector.detect(frame)

        # Kalman Filtering
        trackers = {}
        for detection in detections:
            object_id = detection.id 

            # 1. Check for Unexplored or Low-Confidence Objects:
            if object_is_unexplored(object_id) or object_confidence_is_low(object_id):
                # Capture image of the object
                image = capture_object_image(object_id)

                # Query Gemini for object identification and description
                gemini_response = query_gemini(image, "What is this object? Describe it.")

                # 2. Update Object Graph with Gemini Response:
                learner.update_object_graph(object_id, gemini_response) 

                # 3. Decide Exploration Based on Gemini Response:
                if should_explore_object(object_id, gemini_response): 
                    explore_object(object_id)

            # 4. Kalman Filtering (for ALL objects, even if explored):
            if object_id not in trackers:
                trackers[object_id] = KalmanTracker(detection.state) 
            predicted_state = trackers[object_id].predict()
            updated_state = trackers[object_id].update(detection.state)

            # 5. Update the graph with alpha (for ALL objects):
            learner.update_graph(object_id, updated_state, predicted_state, alpha=0.5) 

        # Create Graph for GNN
        graph = Data(
            x=torch.tensor([tracker.state for tracker in trackers.values()]).float(),  # State features
            edge_index=torch.tensor(list(learner.object_graph.edges())).T.long() # Convert to PyTorch tensor and ensure correct shape
        )

        # Train GNN
        if len(trackers) > 1: # Only train if there are multiple objects
            gnn_model.train()
            optimizer_gnn.zero_grad()
            future_predictions_gnn = gnn_model(graph.x, graph.edge_index)
            loss_gnn = torch.mean((future_predictions_gnn - graph.x)**2) # Example MSE loss
            loss_gnn.backward()
            optimizer_gnn.step()

        # Prediction using GNN
        gnn_model.eval()
        future_predictions_gnn = gnn_model(graph.x, graph.edge_index)

        # Neural Network Learning (RNN)
        rnn_predictions = [] 
        for object_id, tracker in trackers.items():
            past_states = [tracker.state for _ in range(learner.prediction_horizon)]  # Collect the last 'N' states
            future_predictions_rnn = learner.predict(np.array([past_states]))
            rnn_predictions.append(future_predictions_rnn)
        rnn_predictions = np.array(rnn_predictions)
        
        # Combine predictions from RNN and GNN
        if len(rnn_predictions) > 0 and len(future_predictions_gnn) > 0:
            # Get confidence scores (example)
            rnn_confidences = np.mean(np.square(learner.model.predict(np.array([past_states])) - np.array([past_states])), axis=1)  # Example: Mean squared error of predictions vs. actual states
            gnn_confidences = torch.mean(torch.square(gnn_model(graph.x, graph.edge_index) - graph.x), axis=1).numpy()  # Example: Mean squared error of GNN predictions vs. actual states

            # Get contextual information (example)
            environment_info = get_environment_info()  # Implement this function to get environment data
            robot_state = get_robot_state()  # Implement this function to get robot state data

            # Process Lidar data to identify obstacles (example)
            obstacles = process_lidar_data(environment_info) 

            # Get waypoints from the combiner
            waypoints = combiner([rnn_predictions, future_predictions_gnn, rnn_confidences, gnn_confidences, environment_info, robot_state, obstacles])

            # Smooth and refine the waypoints (optional)
            smoothed_path = smooth_path(waypoints)

            # Execute the path
            execute_path(smoothed_path)

        # Periodically Save Graph Data to Cloud Storage
        if frame_count % 100 == 0:  # Save every 100 frames (adjust as needed)
            save_graph_to_cloud_storage(learner.object_graph, 'your-bucket-name', 'graph_data.json')

        # ... (Visualization, etc.)

# Placeholder functions for environment and robot state
def get_environment_info():
    # Implement logic to capture environment information
    # Example: Read sensor data, process camera images, etc.
    return np.array([0.0, 0.0, 0.0])  # Replace with actual environment data

def get_robot_state():
    # Implement logic to get robot state information
    # Example: Read robot position, orientation, velocity, etc.
    return np.array([0.0, 0.0, 0.0])  # Replace with actual robot state data

# ... (Other placeholder functions for object detection, Gemini, exploration, etc.)

# Initialize and Run the Robot Loop
capture = cv2.VideoCapture(0)  # Open camera (or your video source)
robot_loop()