Welcome to FIGAROH's documentation!¶
FIGAROH (Free dynamics Identification and Geometrical cAlibration of RObot and Human) is a comprehensive Python toolbox for robot calibration and identification.
Key Features¶
- Dynamic parameter identification for rigid multi-body systems
- Geometric calibration for serial and tree-structure robots
- Reporting & verification (V&V) suite — terminal and self-contained
HTML diagnostic reports, a machine-readable pass/fail verdict
(
verify()), and a static two-run compare page. See Reporting & Verification. - Advanced linear solver with 10 methods (lstsq, QR, SVD, Ridge, Lasso, Elastic Net, Tikhonov, constrained, robust, weighted)
- Regularization and constraint optimization (L1/L2 regularization, box constraints, linear equality/inequality)
- Unified configuration system with template inheritance
- Advanced regressor computation with object-oriented design
- Support for URDF modeling convention
- Pluggable dynamics backends (Pinocchio, MuJoCo, Genesis, Isaac Sim)
- Optional physical-consistency projection and base→full parameter reconstruction for identification
- Extensive examples and tutorials covering UR10, TIAGo, TALOS, and Staubli TX40
Quick Links¶
Where to start¶
| I want to... | Go to |
|---|---|
| Install FIGAROH and run my first calibration/identification | Getting Started |
| Understand the theory behind calibration/identification/optimal design | Tutorials |
| Understand how the library is structured, or how backends/config work | Concepts |
| Generate quality reports and CI-gateable pass/fail verdicts | Reporting & Verification |
| See a complete, runnable example for my robot (or a similar one) | Examples Gallery |
| Look up a specific class or function | API Reference |
| Check what's planned, what changed, or troubleshoot an issue | Further Reading |