Teach a KUKA LBR iiwa 7 R800 to pick, carry, and place
Seven joint target curves plus one gripper-width curve, sampled at sixteen knots: 128 variables optimized against a piecewise physical objective that never leaks a gradient.
Frankensim household flagship
Optimize a complete pick-and-place
Seven joint target curves plus one gripper-width curve, each sampled at sixteen knots:(7 joints + 1 gripper) × 16 = 128 variables
CMA-ES receives only a scalar receipt after a full rollout. Compliant contact, stick/slip friction, free object dynamics, release, hard limits, and owner-routed obstacle/self/object separation make the objective piecewise and black-box—there is no browser gradient hiding behind the animation.
At 128 dimensions, all four representations fit the honest browser envelope. Full CMA is pedagogically useful here; it is rightly refused for the 5,040-D walking problem.
All variants · one physical objective
Which covariance model helps at 128-D?
Run every owner implementation from the identical curriculum, seed, population, and rollout budget. This is a local measurement on one nonsmooth task—not a universal ranking.
Full CMA-ES
Learns every pairwise covariance interaction
O(n²) state · O(n³) decomposition
Separable CMA-ES
Learns one independent scale per coordinate
O(n) state · O(n) update
LM-CMA
Remembers a bounded history of search directions
O(mn) state · O(m²n) worst-case update
LM-MA
Maintains a bounded moving linear transform
O(mn) state · O(mn) update
What the kernel actually does (and doesn't)
tap to expand
What the kernel actually does (and doesn't)
Modeled
- · 7 revolute DoFs (iiwa topology) with hard joint limits
- · SE(3) FK, inverse-dynamics computed torque
- · Featherstone articulated-body forward dynamics
- · Compliant normal pad force + Coulomb friction
- · Certified convex separation for collision pairs
- · GJK + EPA query count surfaced in the receipt
Simplified
- · Collision uses oriented-box envelopes, not triangle meshes
- · No impulse solver, deformable object, or cable model
- · Grasp pads are finite, rigid, parallel-jaw style
- · Object dynamics are rigid-body only
- · No joint belt-elasticity, backlash, or stiction
- · No multi-arm coordination, bimanual, or human input
Not modeled
- · No slip detection, regrasp, or recovery reflex
- · No inertial measurement, encoder, or actuator lag
- · No environment lighting, occlusion, or camera noise
- · No learned policy beyond the periodic basis
- · No sim-to-real transfer or hardware validation
- · No reachability planner, grasp planner, or motion planner
A placement the kernel approves can still fail on a real KUKA. The page deliberately stops at a deterministic explainer benchmark; treating it as a controller validation would be a category error.
A parametric model with a paper trail
The procedural shell is intentionally mesh-free. Segment endpoints come from owner poses; the table records the pinned source joint-offset magnitude and mass used by dynamics. Orange housings are display geometry; the collision owner independently builds conservative oriented boxes from those source frames.
| source link | joint offset magnitude (m) | mass (kg) |
|---|---|---|
| iiwa_link_0 | base | 5.0000 |
| iiwa_link_1 | 0.1500 | 3.4525 |
| iiwa_link_2 | 0.1900 | 3.4821 |
| iiwa_link_3 | 0.2100 | 4.0562 |
| iiwa_link_4 | 0.1900 | 3.4822 |
| iiwa_link_5 | 0.2100 | 2.1633 |
| iiwa_link_6 | 0.1995 | 2.3466 |
| iiwa_link_7 | 0.1012 | 3.1290 |
Pinned community-reference source: iiwa7.xacro at revision 44f9d13. It is not a KUKA certification artifact.
Why this is a useful black-box flagship
1 · Reach and close
The seven joint splines must align both finite pads with the object while the finger spline is actually closing.
2 · Earn the grasp
Frankensim integrates compliant normal force, friction, object translation, and object rotation. Nothing is latched or teleported.
3 · Lift, route, release
The object must clear 9 cm, reach the goal tolerance, finish released on support, and avoid owner-reported obstacle, self, and proximal-object collision risk.
The source-feasible curriculum makes the demo inspectable from first paint, while live CMA-ES still searches every coordinate. If a sampled policy drops the object or misses the station, the receipt says so; the renderer cannot substitute a canned success animation.
Why 128 dimensions is the honest sweet spot
Seven joint target curves and one gripper-width curve are sampled at sixteen knots — 128 CMA-ES variables. The objective is assembled from a full LBR iiwa 7 R800 rollout with contact activation, static-slip friction capacity, free-space dynamics, release timing, hard limits, and an owner-routed obstacle/self/object separation — piecewise and black-box by construction. There is no browser-side gradient to hide behind, and the grasp is only claimed when the receipt verifies it.
At 128 dimensions all four scalable representations (Full, Separable, LM-CMA, LM-MA) fit the honest browser envelope, so this section is where the family race is physically meaningful — Full CMA is pedagogically useful here and rightly refused for the 5,040-D humanoid walking problem.