Mechatronics on Mulham Fetna

From an Onshape assembly to a MuJoCo model with onshape-to-robot

contact@mulhamfetna.com (Mulham Fetna) — Wed, 16 Sep 2026 00:00:00 +0000

The simulated hand was never modelled by hand. It is an Onshape assembly — five SG90 servos, fifteen knuckle mates, a palm full of tendon channels — pulled through the Onshape API and written out as MuJoCo XML. Here is the design, and every setting that steers the export.

Open the Onshape assembly

The design
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Palm and fingers: four three-phalanx fingers and a three-segment thumb, every knuckle a revolute mate with limits. The RGB triads in the views are mate connectors.
Tendon channels: one per finger, running down the palm into the base.
Servo block: five SG90-class servos, staggered so each horn sits under a tendon exit.

The design has a history
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Start

2026-09-02

The first version in the history.
v1.0.0 — MediaPipe

2026-09-06

The joint-angle-driven hand behind the RViz predecessor project.
v1.0.1 → Main

2026-09-08

Point release and the main line the later work branches from.
V3 → Mujoco branch

2026-09-16

The current design used by this twin — the version with the servo base block shown above.

Onshape version history	Mate features

43 part instances, 112 mate features: the 15 dof_* knuckle mates, the servo mates, and many Fastened mates.

From tutorial project to production robot: a roadmap for the tendon hand twin

contact@mulhamfetna.com (Mulham Fetna) — Wed, 16 Sep 2026 00:00:00 +0000

The system works, and it teaches well. Getting it to drive real servos safely is a sequence of well-scoped upgrades — each one grounded in a limitation measured earlier in this series.

Where it stands
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Area	Today	Production target
Hand tracking	image-normalized landmarks, one global calibration	metric world landmarks, per-finger calibration, a temporal filter
Command mapping	flexion → ±50 N; the twin is a switch	flexion → tendon length; proportional curl
Physics model	force motors, decorative horns, no self-contact	position servos on horns, tuned stiffness, contacts for grasping
Middleware	one topic, default QoS, open on the LAN	parameters, explicit QoS, a watchdog, SROS 2
Containers	privileged, host namespaces, root, `xhost`	least privilege, non-root, optional headless
Code	classes copied into three files, no tests	one shared package, tests, CI
Hardware	simulation only	a servo driver on the same topic

Stage 1 · Correctness

Measure the right thing, command the right quantity

World landmarks. Image-normalized coordinates bend angles by up to 16° with hand orientation (Part 4). Read multi_hand_world_landmarks instead, then recalibrate.

The manual MJCF edits that turn a CAD export into a tendon-driven hand

contact@mulhamfetna.com (Mulham Fetna) — Wed, 16 Sep 2026 00:00:00 +0000

A raw CAD export gives you bodies, joints, meshes and sites — and a hand that does nothing. Four edits and one extra file turn it into a tendon-driven twin. One of those edits, it turns out, does nothing at all — which is worth understanding too.

Which edits survive a re-export
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flowchart TB
    subgraph generated["robot.xml — regenerated by onshape-to-robot"]
        D["① joint defaults
manual — re-apply"]
        T["② tendons + ③ contacts
auto-injected from tendons.xml"]
        B["bodies · joints · sites · meshes
generated"]
        A["④ actuators
manual — re-apply"]
    end
    S["⑤ scene.xml
floor · lights · skybox
never regenerated"] -->|"include robot.xml"| generated

#	Edit	Lives in	Survives re-export?
①	Joint friction / armature / damping defaults	`robot.xml` `<default>`	✘ re-apply
②	Flexor + extensor spatial tendons	`tendons.xml` → injected	✔
③	Contact exclusions	`tendons.xml` → injected	✔
④	Five tendon motors	`robot.xml` `<actuator>`	✘ re-apply
⑤	Environment	`scene.xml`	✔ separate file

tendons.xml is kept byte-identical to the <tendon> and <contact> region of robot.xml. Tune a tendon in robot.xml without copying it back and the next export silently reverts it.

① Joint defaults — stability
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<default class="ros2-tendon-driven-hand-gazebo-digital-twin">
  <joint frictionloss="0.001" armature="0.0001" damping="0.01"/>

Attribute	Value	Role
`damping`	0.01 N·m·s/rad	stops a 2 g phalanx reaching absurd speed when 50 N yanks it
`armature`	0.0001 kg·m²	rotor-like inertia on each joint’s diagonal — conditions the solver for very light bodies
`frictionloss`	0.001 N·m	a small dry-friction dead-band so joints settle instead of creeping

Phalanges weigh 1.8–5.4 g. Without these, tiny inertias under large tendon forces blow up the integrator — an earlier, larger revision of the model logged Nan, Inf or huge value in QACC at DOF 128. The simulation is unstable.

Underactuation: why three knuckle angles become one tendon command

contact@mulhamfetna.com (Mulham Fetna) — Wed, 16 Sep 2026 00:00:00 +0000

avg_angle = np.mean(angles) looks like noise filtering. It isn’t. It is a mechanical decision: a human finger has three joints you can move separately, and this robot finger has one string.

Not smoothing — compression
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“Averaging” in a sensor pipeline usually means averaging over time to reduce noise. Nothing here keeps history between frames. The mean is taken across space — over the three joints of one finger in a single frame — to solve a problem called underactuation.

The 3-to-1 problem
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	Human finger	Robot finger
Joints	3 (MCP, PIP, DIP)	3 hinges (`_mcp`, `_pip`, `*_dip`)
Independent actuators	many muscles; joints move semi-independently	1 flexor tendon, 1 motor
Degrees of freedom you can command	~3	1

A system with fewer actuators than joints is underactuated. A single tendon threads all three joints of each robot finger, so the only command is “pull this string with force F” — and the joints share that pull according to routing geometry and dynamics.

One string, six via-points, three joints. Pull it and all three knuckles move together.

So the vision layer must compress three human measurements into one robot command.

A webcam, some vector geometry, and a hand that moves

contact@mulhamfetna.com (Mulham Fetna) — Tue, 08 Sep 2026 00:00:00 +0000

Everything in this series in one read: how a $20 webcam ends up driving a 15-DOF CAD model in real time, why every step is deliberately explicit rather than learned, and what broke along the way.

You hold your hand up to a laptop camera. On the other half of the screen, a robotic hand — designed in CAD, never manufactured — closes its fingers at the same moment yours do.

There is no glove, no marker, no depth sensor. Just an RGB webcam, two small neural networks, about forty lines of vector geometry, and a middleware stack that thinks it is talking to a real robot.

All of it is open source under AGPL-3.0 and archived with a DOI: 10.5281/zenodo.22658556.

flowchart LR
    A["📷 Webcam
/dev/video0"] --> B["BlazePalm
palm detector"]
    B --> C["Landmark regressor
21 × (x, y, z)"]
    C --> D["Dot-product geometry
15 interior angles"]
    D --> E["Normalize → flexion
0.0 straight · 1.0 curled"]
    E --> F["Lerp onto the URDF's
mechanical limits"]
    F --> G["/joint_states"]
    G --> H["robot_state_publisher
→ /tf"]
    H --> I["🖥️ RViz digital twin"]

The rule that shaped the build
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There is an easier version of this project. Collect a few thousand frames of a hand next to the corresponding CAD poses, train a network to map one to the other, and let gradient descent work out the relationship.

Code-to-CAD: Generating a 15×15 Microneedle Array Four Ways

contact@mulhamfetna.com (Mulham Fetna) — Mon, 07 Sep 2026 00:00:00 +0000

The same 15×15 microneedle array, generated four ways in code. The interesting part isn’t the loops — it’s that three of the four scripts refuse to merge the 225 cones into the baseplate at all, and hand that job to the slicer instead.

Beyond the Mold: The Manufacturing Constraints of Bone Scaffolds

contact@mulhamfetna.com (Mulham Fetna) — Sat, 29 Aug 2026 00:00:00 +0000

The manufacturing route behind a published bone scaffold, and an honest comparison against direct ink writing, electrospinning and vat photopolymerisation.

Treating a Biomaterial Like a Machine: The Closed-Loop Bone Scaffold

contact@mulhamfetna.com (Mulham Fetna) — Sat, 29 Aug 2026 00:00:00 +0000

Sensor, controller, actuator — all three built into a crystal lattice. What a triple-doped hydroxyapatite scaffold looks like when you read it as a control system.

Mechatronics on Mulham Fetna

From an Onshape assembly to a MuJoCo model with onshape-to-robot

The design #

The design has a history #

Start

2026-09-02

v1.0.0 — MediaPipe

2026-09-06

v1.0.1 → Main

2026-09-08

V3 → Mujoco branch

2026-09-16

From tutorial project to production robot: a roadmap for the tendon hand twin

Where it stands #

Stage 1 · Correctness

Measure the right thing, command the right quantity

The manual MJCF edits that turn a CAD export into a tendon-driven hand

Which edits survive a re-export #

① Joint defaults — stability #

Underactuation: why three knuckle angles become one tendon command

Not smoothing — compression #

The 3-to-1 problem #

A webcam, some vector geometry, and a hand that moves

The rule that shaped the build #

Code-to-CAD: Generating a 15×15 Microneedle Array Four Ways

Beyond the Mold: The Manufacturing Constraints of Bone Scaffolds

Treating a Biomaterial Like a Machine: The Closed-Loop Bone Scaffold

The design
#

The design has a history
#

Where it stands
#

Which edits survive a re-export
#

① Joint defaults — stability
#

Not smoothing — compression
#

The 3-to-1 problem
#

The rule that shaped the build
#