Pollen Robotics, a subsidiary of Hugging Face, has opened pre-orders for the Microduck, starting at $399, excluding tax and shipping. The goal is to ship to North America, Europe, and the UK before Christmas, with color options in cream, graphite, lavender, and sky.

This armless bipedal robotic duck stands 25 cm tall and weighs less than 800 grams. It features 15 motors, a camera, a mini LiDAR, two sets of inertial measurement units, a microphone, a speaker, Wi-Fi, and Bluetooth, and can grasp objects weighing around 800 grams with its beak. The battery capacity is 2600 mAh, providing about one hour of runtime. It has a 50 Hz onboard strategy loop, a Rockchip RK3566 processor, 1GB of RAM, and 32GB of storage.

It comes pre-loaded with seven trained actions: swaying walk, sitting and standing, kicking a ball, pecking at objects, wheel sliding, and self-righting after common falls. It includes a game controller, can chase a laser pointer, and responds to its surroundings. Each unit generates a unique sound upon first startup. Demonstrations show it pecking up dirty socks and markers, and resetting after being pushed over.

A software development kit, MuJoCo simulation, and complete reinforcement learning training stack are available under the Apache 2.0 license on the code platform, allowing training in nearly 4096 parallel environments either locally or on Hugging Face's computing tasks, with the ability to export to ONNX for deployment on real hardware. Pollen notes that mechanical and electronic design files are not open source. It is ready to play out of the box, targeting developers who want to train new actions through trial and error.

Pollen was founded in 2016 by former researchers from Bordeaux, France, and was integrated into Hugging Face in April 2025, becoming part of its robotics team. Its previous desktop robot, Reachy Mini, was priced at $499 for the full version and $399 for the lightweight version, focusing on interaction; Microduck shifts towards mobility and fall recovery. CEO Clem Delangue stated this is an affordable open-source robot that can learn new tricks through reinforcement learning, aimed at lowering the barriers for physical AI and world models, targeting around 50,000 units and approximately $20 million in revenue.

Pre-order funds will go towards the complete machine, accessory packages, and subsequent cloud training time. Buyers are developers and geeks looking to transfer simulations to reality on the actual machine, while sellers aim to extend the model community to desktop hardware. The event-driven product rollout is not a large laboratory order. Beneficiaries include low-cost data collection and strategy sharing networks; those under pressure are the quadrupedal and humanoid complete machines and closed training kits that cost tens of thousands of dollars. The cost of falling has been reduced to desktop impacts, moving trial and error from laboratories into living rooms.

Source: Public Information