Robotics is a branch of AI involving the design, construction, and use of robots. It intersects with computer science, mechanical and electrical engineering, and helps machines operate efficiently and autonomously in complex environments.


Think of “Robotics” like teaching a toy car how to drive by itself. You build the car, give it a brain (computer), and teach it the rules of driving. The car then uses its new knowledge to drive around without your help. That’s what robotics in AI does, it builds machines (like our toy car) that can operate and learn on their own.

In-depth explanation

Robotics, in the context of AI, refers to the intersection of machine learning, hardware engineering, and robotics science to design, develop, and deploy robots capable of performing tasks with minimal human intervention. The key AI components fundamental to Robotics are perception and control.

Perception includes techniques such as computer vision and sensor fusion to help the robot understand the world around it. Computer vision uses algorithms to interpret and understand the visual data, while sensor fusion seamlessly integrates the data coming from multiple sensors to generate more accurate and reliable insights.

Control in robotics refers to the algorithms and procedures that determine how a robot executes tasks. This includes path planning, grasping techniques for manipulation, and locomotion strategies. To execute these tasks efficiently, robots often employ Reinforcement Learning, a type of machine learning where the robot learns optimal actions through trial-and-error, and gets better over time.

The field of robotics also embraces the concept of Embodied AI, which integrates cognitive computing abilities into a physical entity enabling it to interact with the real world. Key applications of Robotics in AI span multiple industries including healthcare (surgical robots), manufacturing (automated assembly lines), agriculture (automated harvesters), and many more.

Moreover, Robotics is shifting more toward a multi-robot system approach, where a group of robots work together to carry out tasks more efficiently. Studying the communication and operations of these robots could prove vital to creating reliable and efficient robotic systems.

Artificial Intelligence, Embodied AI, Machine Learning (ML),, Reinforcement Learning (RL),, Multi-Robot Systems, Computer Vision, Sensor Fusion, Autonomous Systems