Guides
Long-form explainers on the AI, hardware, and software powering modern robots. Each guide links to the Robot Brain Index entries it references.
Best robotics simulation tools
A head-to-head on the simulators that matter — Isaac Sim, MuJoCo, Genesis, Gazebo, PyBullet — and which to use when.
How robots learn from demonstration
Imitation learning is the dominant way modern robots acquire new skills. Here's what's in the toolkit, what's improving, and where it falls short.
Humanoid robots — real vs. hype
What humanoid robots actually do today, what they claim to do, and how to tell the difference when you read a press release.
Robot learning datasets, explained
An overview of the open datasets training modern robot models — what they contain, what they're missing, and how to use them.
Robotics for AI developers
If you're comfortable with PyTorch and transformers but new to robots, here's the shortest viable path to running a real or simulated robot.
Simulation-to-real, explained
Why simulation is the dominant training environment for modern robots, what makes a sim policy survive deployment, and where the gap still bites.
The autonomous robot stack
A working map of the layers between a sensor packet and a motor command in a modern autonomous robot — and what's changing under each layer.
Vision-language-action (VLA) models, explained
VLAs are the family of robot models that take a camera image plus a natural-language instruction and emit motor actions. Here's how they work and where they fail.
What is a robot foundation model?
A foundation model is a single, large model trained on a lot of data that's then adapted to many downstream tasks. Here's what that looks like when the downstream task is moving a body.
What is embodied AI?
A working definition of embodied AI, what makes it different from text-only AI, and the stack that's emerging beneath it.