Machine Learning (ML): Enables robots to learn from data and improve their performance over time without explicit programming. For example, robots can learn to recognize objects, navigate spaces, or optimize tasks through reinforcement learning.

Computer Vision: Allows robots to interpret visual information from cameras or sensors. This is critical for tasks like object recognition, facial recognition, and autonomous navigation.

Natural Language Processing (NLP): Empowers robots to understand and respond to human language, enabling human-robot interaction in applications like customer service or healthcare.

Sensor Fusion: Combines data from multiple sensors (e.g., LiDAR, cameras, accelerometers) to create a comprehensive understanding of the environment, essential for autonomous vehicles and drones.

Robotic Process Automation (RPA): Uses AI to automate repetitive tasks in industries like finance and logistics, improving efficiency and reducing human error.

Edge AI: Processes data locally on the robot rather than relying on cloud computing, enabling faster decision-making and reducing latency.