Robotics AI

Robotics AI is the application of artificial intelligence to physical robots — machines that can sense, decide, and act in the real world. While AI in software operates on data and text, robotics AI must deal with the messy, unpredictable physics of the physical environment.

What makes robotics AI different

Software AI processes information. Robotics AI must also deal with:

• Perception: Understanding the physical environment through cameras, sensors, lidar, and touch sensors
• Planning: Deciding what actions to take to achieve a goal, considering physical constraints
• Control: Translating high-level plans into precise motor commands
• Safety: Operating around humans without causing harm

Each of these challenges is harder in the physical world than in software because the real world is continuous, noisy, and unforgiving of errors.

Current applications

• Manufacturing: Industrial robots using computer vision to inspect products, pick and sort items, and adapt to variations in materials
• Warehousing: Autonomous mobile robots that navigate warehouses, pick orders, and manage inventory (Amazon, Ocado)
• Healthcare: Surgical robots that assist with precision procedures, rehabilitation robots, and autonomous disinfection systems
• Agriculture: Robots that plant, monitor, and harvest crops using computer vision to identify ripe produce
• Delivery: Autonomous vehicles and drones for last-mile delivery

The role of AI in modern robotics

Traditional industrial robots are pre-programmed to repeat exact movements. AI-powered robots can adapt to new situations. A traditional robot arm follows the same welding path every time. An AI-powered robot arm can adjust its path based on what it sees, handling variations in part position or shape.

Foundation models for robotics

A major emerging trend is the development of foundation models for robotics — large models trained on diverse physical interaction data that can generalise across tasks and environments. Google's RT-2 and other research projects aim to give robots the kind of general-purpose intelligence that LLMs brought to language tasks.

Challenges

Robotics AI faces unique challenges: ensuring safety around humans, operating reliably in unstructured environments, handling the high cost of physical hardware, and bridging the sim-to-real gap — the difference between simulated training environments and the messy real world.