Simulation stage

RoboDuck

A small biped robot. We start from Pollen Robotics' Microduck and the Open Duck Mini project, both Apache-2.0 for code, and change what we can measure and improve.

How it learns

POLICYneural netMUJOCO SIMrandomized friction, mass, backlashREWARDvelocity tracking, fallsRL UPDATEPPOACTIONSROLLOUTSEXPORT ONNX → SCORE AGAINST WRITTEN GATES

Clips Simulation

Where it started

RoboDuck CAD render (work in progress)

Pollen's Microduck: 25 cm, 800 g, 15 motors, camera, LiDAR and two IMUs, with a 50 Hz onboard policy trained in MuJoCo. Microduck's STL meshes are CC BY-NC-SA 4.0 (non-commercial), so RoboDuck needs its own geometry for anything sold.

Results so far Simulation

DateRunSettingResult
2026-10-22Earlier Open Duck Mini walk policy (before Stage B), robustnessNominal randomization, commands 0.1 / 0.2 / 0.3 m/s, 15 trials eachVelocity tracking 95% / 95% / 67%, 0 falls
2026-10-23Stage B policy evalNominal, commands 0.2 / 0.3 / 0.4 m/s, 10 trialsMeasured 0.192 / 0.259 / 0.293 m/s, 0 falls
2026-10-23Stage B policy evalStress set, same commands0.147 / 0.210 / 0.224 m/s, 0 falls
2026-10-23Slow walk, 0.1 m/sCommand floor 0.12, 3 seeds × 10 runsTracking 101.5% ± 2.1, 0 falls, 3/3 seeds pass

What's next

Next policy gate: at least 85% velocity tracking at 0.3 m/s under 1.0–1.5° backlash, 3 seeds × 10 runs, zero falls, no loss of turning. Then a second simulator (Isaac Lab) for cross-checks, our own parametric CAD, and an open BOM.

Credit: pollen-robotics/microduck and Open_Duck_Mini, Apache-2.0.