Machine learning in robotics combines the fields of AI and robotics to enable robots to learn and adapt from data.

It allows robots to acquire knowledge, make predictions, and improve performance through data-driven algorithms and techniques.

Machine learning in robotics can be used for various tasks, including perception, control, planning, and decision-making.

It enables robots to perceive and interpret their environment using techniques such as computer vision and sensor fusion.

Reinforcement learning is commonly used in robotics to train robots to perform actions and learn optimal behaviors through trial and error.

It enables robots to adapt and learn from interactions with humans or other robots.

It enables robots to learn from historical data and past experiences, allowing them to improve performance over time.

Machine learning algorithms can be used to optimize resource allocation and energy management in robotic systems.