# Tutorials

These walkthroughs explain the *why* and *how* behind FIGAROH's four core workflows, using TIAGo (a mobile manipulator) as the running example. Each one is backed by a real, runnable script in [figaroh-examples](https://github.com/thanhndv212/figaroh-examples) — see the [Examples Gallery](https://thanhndv212.github.io/figaroh-plus/examples/index.md) for the complete per-robot reference implementations (UR10, TIAGo, TALOS, Staubli TX40) once you've read the walkthrough for the workflow you need.

| Tutorial                                                                                                                | Workflow                              | Answers                                                                        |
| ----------------------------------------------------------------------------------------------------------------------- | ------------------------------------- | ------------------------------------------------------------------------------ |
| [Calibration Walkthrough](https://thanhndv212.github.io/figaroh-plus/tutorials/calibration_walkthrough/index.md)        | Kinematic calibration                 | Why do robots need calibrating, and how does FIGAROH solve for the correction? |
| [Choosing What to Estimate](https://thanhndv212.github.io/figaroh-plus/tutorials/calibration_estimation_guide/index.md) | Kinematic calibration                 | Which corrections should I estimate, and how, for my robot and postures?       |
| [Identification Walkthrough](https://thanhndv212.github.io/figaroh-plus/tutorials/identification_walkthrough/index.md)  | Dynamic parameter identification      | How does FIGAROH turn a torque/motion log into a validated dynamic model?      |
| [Optimal Experiment Design](https://thanhndv212.github.io/figaroh-plus/tutorials/optimal_design/index.md)               | Optimal configurations & trajectories | How does FIGAROH decide *which* poses/motions to measure, instead of guessing? |

## Starting with a new robot or dataset

Begin with [Plan, Fit and Validate](https://thanhndv212.github.io/figaroh-plus/example_workflow/index.md) to inventory data, choose the model/methods, design acquisition, process measurements and establish independent validation. These task walkthroughs start after those decisions; the examples repository supplies a reusable experiment brief and integration guide.

## Prerequisites

- FIGAROH installed (see [Getting Started](https://thanhndv212.github.io/figaroh-plus/getting_started/index.md))
- A robot URDF and a [unified config](https://thanhndv212.github.io/figaroh-plus/concepts/configuration/index.md) for it — or clone [figaroh-examples](https://github.com/thanhndv212/figaroh-examples) and use one of the shipped robot folders directly
- Basic familiarity with the linear-in-parameters formulation `τ = W(q, q̇, q̈) · φ` for dynamics; geometric calibration instead fits pose/contact residuals and analyzes their parameter Jacobian

## The four workflows, at a glance

```
Optimal Configuration Generation ──▶ (collect calibration data) ──▶ Kinematic Calibration
Optimal Trajectory Generation    ──▶ (collect identification data) ──▶ Dynamic Identification
```

The two design steps are optional tools for choosing informative, feasible experiments (see [Optimal Experiment Design](https://thanhndv212.github.io/figaroh-plus/tutorials/optimal_design/index.md)). Acquisition coverage, synchronization and sensor quality still limit the resulting fit.

Use verification and HTML reports where supported by the selected workflow — see [Reporting & Verification](https://thanhndv212.github.io/figaroh-plus/reporting_and_verification/index.md). Inspect skipped checks and actual held-out data; a passing verdict alone is not proof of a validated model.