CMA-ES:
My favorite black-box optimizer
When gradients are cheap and smooth, Adam and SGD excel. But when gradients do not exist, suffer from severe noise, or cost hours per evaluation, such as in aircraft CFD, suspension bridge FEA, or discrete neural architecture search, CMA-ES is the standard workhorse.
Instead of stepping a single point downhill, CMA-ES maintains a Gaussian search distribution, shifting its mean toward better samples and bending its covariance matrix toward a multiple of the objective's inverse Hessian, discovering landscape curvature without calculating derivatives.
g(f(x)).Ax + b once the initial distribution is transformed to match.H⁻¹C approaches a multiple of the inverse Hessian without derivatives.What CMA-ES is and why anyone should care
When gradients disappear
A concrete CMA-ES walk-through: designing an airplane wing
Two high-performance CMA-ES engines in Rust
Live CMA-ES landscape explorer (TypeScript + WASM)
Inside the optimizer: covariance geometry in 3D
Real-World Robotics: Whole-Body Humanoid & Articulated Arm
Unitree G1 Whole-Body Walking Simulation
Watch a 29-DoF Unitree G1 humanoid optimize a 5,040-parameter locomotion policy live in your browser. Features whole-house 3D waypoint navigation across 7 rooms, zero-penetration collision detection, and ragdoll dragging.
- Whole-house multi-room waypoint navigation routes
- Touch & ragdoll dragging with continuous collision detection
- 480 Hz articulated dynamics & terrain push balance recovery
KUKA LBR iiwa 7 R800 Pick-and-Place
A source-bound 7-DoF KUKA LBR iiwa 7 R800 is optimized to reach, grasp, transport, and release objects in three reduced household scenes. Placement is accepted only when the collision receipt is clear.
- 3D Coulomb friction cones & contact force verification
- KMR iiwa 4-mecanum mobile base navigation & 2D LiDAR raycasting
- Interactive 360° camera orbit & grasp microscope inspection