AI and Robotics

Artificial Intelligence is the thinking core of robotics. While motors, sensors, and mechanics allow robots to move and act, AI determines how they perceive the world, make decisions, and adapt to change. The history of AI and robotics reflects changing ideas of intelligence itself—from explicit rules to learning, from reasoning in symbols to experience-driven behavior.

Shakey

A landmark in this history is Shakey, developed in the late 1960s. Shakey was the first robot to integrate perception, reasoning, and action using Artificial Intelligence. It constructed symbolic representations of rooms and objects, planned sequences of actions, and executed them in the physical world. Shakey demonstrated that intelligence could be computational: the robot thought before it moved. Although extremely slow and limited, it defined the foundation of AI-driven robotics.

Symbolic AI: Thinking in Rules

Early AI assumed intelligence could be described using logic and symbols. Robots were given explicit knowledge about the world—objects, locations, actions—and used planners to compute what to do next. This approach made reasoning transparent and explainable, but fragile. Real environments are noisy, uncertain, and incomplete, and symbolic systems struggled when their internal models no longer matched reality.

Despite these limits, symbolic AI introduced key ideas still used today: world models, planning, goals, and decision-making under constraints.

From Hand-Coded Intelligence to Learning Systems

As data and computing power grew, AI shifted toward machine learning. Instead of encoding intelligence by hand, systems learned patterns from examples. For robotics, this enabled perception systems that could recognize objects, faces, gestures, and speech—tasks that were nearly impossible to define with rules alone.

Learning-based AI also changed control and decision-making. Through techniques such as reinforcement learning, robots could learn actions by trial and error, improving through experience rather than fixed programming. Intelligence became adaptive rather than predefined.

Embodied AI

Modern robotics emphasizes embodied intelligence: intelligence arises from the interaction between AI algorithms, the robot’s body, and its environment. Perception, learning, planning, and control are tightly coupled. Robots continuously update their understanding of the world and adjust their behavior in real time.

This approach supports:

• Robots that adapt to new tasks without reprogramming
• Safe collaboration between humans and machines
• Long-term autonomy in complex, dynamic environments

AI-Driven Robots Today

Today’s robots combine symbolic reasoning, learning, and real-time control. AI enables robots to explain decisions, predict outcomes, and cooperate with humans. As robots gain more autonomy, questions of trust, responsibility, and ethics become central.

From Shakey’s symbolic plans to today’s data-driven intelligence, AI has transformed robots from automated machines into adaptive agents—systems that not only act in the world, but increasingly understand it.