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AI Robotics

Where AI meets the physical world

AI-Powered Robotics

Robotics is where AI becomes physical. Modern robots use AI for perception, decision-making, and learning—enabling them to operate in complex, unstructured environments.

The Hard Problem

"It's easy to make a computer play chess, but hard to make a robot that can pick up the pieces." Physical world tasks are surprisingly difficult for AI.

Key Capabilities

Perception

Robots understanding their environment through cameras, sensors, lidar.

Manipulation

Grasping, moving, and interacting with objects—still a major challenge.

Navigation

Moving through spaces while avoiding obstacles. Powers self-driving cars and delivery robots.

Learning

Improving through experience, often using reinforcement learning.

Types of AI Robots

Industrial Robots

Factory automation: welding, assembly, packaging. Increasingly AI-powered for flexibility.

Autonomous Vehicles

  • Self-driving cars (Tesla, Waymo, Cruise)
  • Delivery robots
  • Autonomous drones

Humanoid Robots

  • Tesla Optimus — General-purpose humanoid
  • Boston Dynamics Atlas — Acrobatic humanoid
  • Figure — AI-powered humanoid startup

Service Robots

Cleaning robots, warehouse robots (Amazon), hospitality robots.

LLMs and Robotics

The latest frontier: using language models to control robots.

  • Tell a robot what to do in natural language
  • Robot translates language to physical actions
  • Google RT-2 and similar systems

Challenges

  • Real-world complexity — Infinite edge cases and variations
  • Safety — Robots can hurt people and break things
  • Dexterity — Human-level manipulation is hard
  • Generalization — Working in new environments
  • Cost — Hardware is expensive

The Future

  • General-purpose humanoid robots in homes
  • Fully autonomous vehicles
  • Robots learning from videos of humans
  • Surgical robots with superhuman precision

Summary

  • • AI enables robots to perceive, navigate, and manipulate
  • • Types: industrial, autonomous vehicles, humanoids, service
  • • LLMs are being connected to robots for natural language control
  • • Physical-world AI remains harder than digital AI