Phase 6: Physical AI
This is where robotics becomes magical. Physical AI is the marriage of modern deep learning — the same technology behind ChatGPT and image generators — with robots that operate in the physical world. Phase 6 covers how neural networks learn to grasp objects, how reinforcement learning teaches robots to walk, and how foundation models are giving robots something that looks remarkably like common sense.
Why Physical AI is the next frontier
Traditional robotics relied on hand-coded rules: "if sensor reads X, do Y." Physical AI replaces rigid rules with learned behavior. A robot trained with deep learning can generalize — it can pick up an object it's never seen before, in a cluttered environment it's never been in. That's the difference.
1. Deep Learning for Robotics
CNNs for visual grasping, imitation learning from human demonstrations, and visual servoing — where the camera output directly drives motor commands. The core deep learning techniques for physical systems.
Explore Deep Learning for Robotics →2. Reinforcement Learning for Robots
Train a robot to walk, run, grasp, and navigate purely through trial and error — with reward signals. PPO, SAC, and sim-to-real transfer: how RL agents trained in simulation deploy to physical hardware.
Explore RL for Robots →3. Embodied AI & Foundation Models
RT-2, Gato, and PaLM-E — large language and vision models trained to control robots. How giving a robot a "world model" from internet-scale data allows it to generalize to novel tasks with natural language instructions.
Explore Embodied AI →Frequently Asked Questions
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