> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Robotic Arm Design Optimization using a Digital Twin

> Build a simulation twin of the AR4 robot arm from its documents, then use it to score and improve the arm's design on a pick-and-place task.

A robot arm is a long list of design choices. The link lengths decide what the arm can reach. The motors and gear ratios decide how much it can lift and how fast it moves. The material decides how heavy the links are, and the gripper decides what the arm can hold. Change one choice and the others are affected: a longer link reaches farther but puts more load on the motor below it.

Testing each change on a physical prototype is slow and expensive. A digital twin lets you test the change in simulation first. In this project you build a twin of the Annin Robotics AR4, a six-axis desktop arm whose manual, datasheets and kinematic model are public. You then use the twin the way an engineer would: to measure how good a design is, and to find out what a change would do before anyone cuts metal.

## A twin as a design tool

A digital twin is often described as a 3D model that mirrors a machine. To use one for design, you need three properties.

Every number in it has a source. The mass of a link, the limit of a joint and the ratio of a gearbox each point to the sentence, table cell or datasheet that states them. If a number has no source, you cannot trust any result that depends on it.

The geometry is parametric. Each FreeCAD part is driven by named values, such as a link length or a wall thickness, instead of fixed lines drawn by hand. Change a value and the part rebuilds itself.

The simulation reads the same values as the CAD model. When a link gets longer, its mesh, its mass, its inertia and the Newton model all change together. Nothing has to be copied by hand from one tool to another, so nothing drifts out of step.

Together they turn a "what if" question into a measurement. To learn what a 20 mm shorter forearm does to the arm's success rate, you change one value, rebuild, and run the evaluation again.

## An example from aircraft design

Grimm, Price and Ahmad used this loop to design a flying-wing glider (Grimm et al., 2026). A flying wing is hard to make stable, because small changes to its shape cause large changes in how it flies. They built the glider as a parametric model in FreeCAD. At each step an optimizer changed the model's parameters, and OpenFOAM simulated the airflow around the new shape (computational fluid dynamics, or CFD). The simulated airflow gave each design a cost to minimize. Stability was the main goal, and lift and endurance were limits that kept the glider flyable.

After ten iterations the cost fell from 102.0 to 10.3. The design met its stability targets, although it did not fully meet the power and lift targets. The authors then built a prototype from the optimized geometry and flew it, and in flight it was stable, as the simulation had predicted.

Only one glider was built, at the end. Every design before it existed only as a FreeCAD model and a CFD run. The optimizer could change the geometry without a person redrawing it, because the model was parametric. The simulator scored each new shape without a wind tunnel, and the score told the optimizer which change to try next.

## The same loop for a robot arm

Your project has the same structure. FreeCAD holds the arm's parametric parts. Newton, a GPU physics simulator, plays the role that CFD played for the glider. The score comes from a task instead of airflow: the arm picks a cube up off a table and places it in an open box.

```mermaid theme={null}
graph TB
    docs["Documents<br/>manual, datasheets, spreadsheets"]
    kdb["kDB<br/>every value with its source"]
    cad["FreeCAD<br/>parametric parts and assembly"]
    newton["Newton<br/>the arm as a physics model"]
    eval["Pick and place<br/>trained skills, evaluation episodes"]
    score["Score<br/>share of episodes with the cube in the box"]

    docs --> kdb
    kdb --> cad
    kdb --> newton
    cad --> newton
    newton --> eval
    eval --> score
    score -->|"recorded next to the values it used"| kdb
```

The course trains one skill for each step of the task, one to pick and one to place, in Newton on your twin. It then runs many evaluation episodes with the cube and the box in random places. The share of episodes that end with the cube in the box is your design's score.

A twin can cite a source for every mass and limit and still fail at the job. The arm might not reach the box, or the gripper might drop the cube when the arm swings. A checklist of sources misses both failures, and the task score shows them.

The score is written back to the kDB next to the design values that produced it, so over time the kDB records which design scored what. You, or an agent that reads the kDB, can then change a value, rebuild the twin and compare the new score with the old one.

## Grounded numbers

The glider's result could be trusted because the flight test agreed with the simulation. Your twin has no flight test before grading, so its numbers have to earn trust another way. Every value goes into a Knowledge DB (kDB), where it is stored with the evidence that supports it: the document, the page, and the exact sentence or cell. A value that comes from a calculation, such as a mass from a volume and a density, records the formula and the values it used.

Tools check the kDB for you. `okf-validate` and `okf-ground` confirm that every value has a source and that the source says what the kDB claims. `twin-check` confirms that the Newton model uses the kDB's values, and `twin-sync` confirms that the FreeCAD parts are bound to them. When the score changes, you can trace the change to the values that moved.

## What you build and what the course provides

The score grades your arm's design, so everything except the twin is the same for every submission.

You build:

* an agent, in Python with Pydantic AI, that reads the AR4 documents into your kDB and cites each value;
* a CAD agent that models each link in FreeCAD through an MCP server, with every dimension bound to the kDB;
* the FreeCAD assembly of the whole arm with its gripper;
* `twin/build.py`, which builds the Newton model from your parts and your kDB.

The course provides:

* the pick-and-place task, with the cube and the box as fixed task constants;
* the checks that decide when the arm is holding the cube and when the cube is in the box;
* the skill trainer, with a fixed configuration, step budget and set of random seeds;
* the plan executor and the judge that writes the score.

At grading time the course trains the skills on your submitted twin, on course GPUs, and runs the evaluation the same way for everyone. Two submissions then differ only in their twins, so a difference in score is a difference in design.

## Sources you may use

Your twin comes from the AR4's documents: the AR4 MK4 robot manual, Annin's kinematic model and motor calculation spreadsheets, and the datasheets of the motors, gearboxes and materials the manual names. Annin's CAD files and the public AR4 URDF are not sources, because the point is to build the arm from its documents. The servo gripper is the one exception. The manual does not give the gripper's dimensions, so you measure them on Annin's gripper STEP files and cite each measurement.

You build your agents yourself with Pydantic AI. Commercial agent products are not accepted. The model behind your agent runs locally or on OpenRouter. The [repository](https://github.com/pantelis/ar4-digital-twin) explains both options and holds the environment, a starter agent and the tools that check your work.

## References

* Grimm, S., Price, E., Ahmad, A. (2026). *Machine-optimized parametric computer assisted design (CAD) of a stable flying wing UAV glider*. Aerosp. Syst.

***

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