MicroDuck Robot: Engineering and Reinforcement Learning

People are completely missing the point.

MicroDuck is not just a little robot to play with. Here's why.

I think people focus too much on the size and appearance of MicroDuck and

miss the engineering behind it. This tiny bipedal robot stands

about twenty-five centimeters tall, weighs around seven hundred

and eighty grams,

and has fifteen motors controlling its legs, head, neck, and

beak. Inside, it runs on a Rockchip

RK3566 processor with one gigabyte of RAM and

thirty-two gigabytes of storage.

It also carries a camera, an eight-by-eight time-of-flight depth

sensor, and

two IMUs that help it track its movement and orientation.

But the real engineering is in how these parts work together.

MicroDuck uses reinforcement learning to control its movements, with a control loop

running at fifty hertz. Its trained behaviors include walking,

sitting, getting back up after a fall, kicking a ball, and grasping

objects with its beak. You can also control it with a gamepad

or explore its

software and training pipeline. The project connects MuJoCo simulation,

PPO reinforcement learning, and sim-to-real deployment, where a

policy trained in simulation runs on the physical robot.

Developers can study the code, retrain movement policies, experiment with

control, and contribute their own software.

There is one detail people should understand.

The software stack is open source, but the complete official mechanical and

electronic design files have not been released as open source hardware.

That distinction matters if you want to reproduce the robot

yourself.

MicroDuck is a compact platform for studying locomotion, reinforcement

learning, and real-world robot control.

At just twenty-five centimeters tall, it puts problems usually associated with

much larger robots on a desk. The real question is what new

behaviors developers can teach it next.