Embeddings are used in autonomous systems to convert complex, high-dimensional data into structured numerical representations that machines can process efficiently. These vector-based models capture essential features of raw inputs like sensor data, images, or text, enabling systems to recognize patterns, make decisions, and adapt to environments. By transforming unstructured data into a format suitable for machine learning models, embeddings simplify tasks such as object detection, navigation, and real-time decision-making, which are critical for autonomous vehicles, drones, or robots.
In perception tasks, embeddings help autonomous systems interpret their surroundings. For example, a self-driving car processes camera and LiDAR data by converting raw pixels or point clouds into embeddings. These vectors might represent objects (e.g., pedestrians, vehicles) with attributes like position, speed, and size. Convolutional neural networks (CNNs) often generate these embeddings, compressing visual data into meaningful features while reducing computational overhead. Similarly, a drone navigating a forest might use embeddings to distinguish trees from obstacles by analyzing depth sensor data, allowing it to adjust its flight path dynamically.
For decision-making and control, embeddings enable systems to map scenarios to actions. A robot arm in a warehouse might convert joint angles and object positions into embeddings to decide how to grasp an item. In reinforcement learning, embeddings represent states (e.g., a car’s speed, lane position, and traffic density) to predict optimal actions like braking or steering. Embeddings also streamline communication between subsystems: a self-driving car’s path planner might output a trajectory as an embedding, which the control system translates into throttle or brake signals. By abstracting complex inputs into compact, semantically rich vectors, embeddings allow autonomous systems to operate efficiently, generalize across environments, and adapt to real-world variability.