FIGAROH

Free dynamics Identification and Geometrical cAlibration of RObot and Human

FIGAROH is a Python toolbox providing efficient and highly flexible frameworks for dynamics identification and geometric calibration of rigid multi-body systems based on the URDF modeling convention. It supports both serial (industrial manipulators) and tree-structure systems (humanoids, mobile manipulators).

📦 Available on PyPI: pip install figaroh 📖 Version: 0.4.6

Note: This repo is a fork from gitlab repo of which the author is no longer a contributor.

Installation

Quick Installation (Recommended)

Install the core FIGAROH package with all dependencies (except for cyipopt):

pip install figaroh

Development Installation

For development or local installation from source, choose one of these methods:

Method 1: Direct pip installation (Simple)

git clone https://github.com/thanhndv212/figaroh-plus.git
cd figaroh
pip install -e .

Method 2: Conda environment (Recommended for the use of cyipopt)

git clone https://github.com/thanhndv212/figaroh-plus.git
cd figaroh
# Create conda environment with optimization libraries
conda env create -f environment.yml
conda activate figaroh-dev

Examples Repository

git clone https://github.com/thanhndv212/figaroh-examples.git
cd figaroh-examples && pip install -r requirements.txt

Key Features

🔧 Dynamic Identification

• Extended dynamic models: friction, actuator inertia, joint offsets
• Optimal exciting trajectory generation (IPOPT)
• Multiple parameter estimation algorithms
• Physically consistent parameters for URDF updates

📐 Geometric Calibration

• Full kinematic parameter estimation (6 DOF per joint)
• Optimal posture selection via combinatorial optimization
• Support for cameras, motion capture, planar constraints
• Direct URDF model updates

⚙️ Configuration System

• Unified YAML format with template inheritance
• Automatic format detection (legacy compatibility)
• Variable expansion and validation
• Task-specific configs: calibration, identification, optimal trajectory

🛠️ Modern Architecture

• Proper logging (NullHandler pattern for libraries)
• Abstract base classes for extensibility
• Pinocchio 3.x compatibility
• Cross-platform: Linux, macOS, Windows

📊 Reporting & Verification (V&V)

• Self-contained HTML diagnostic reports with an interactive before/after chart — solve(html_report=True) / export_html_report()
• Machine-readable pass/fail verdicts for CI — verify() / export_verification_report(), with overridable quality thresholds
• Static two-run compare page — generate_compare_page() diffs two exported runs offline, with a mandatory compatibility check before overlaying them
• See the Reporting & Verification guide for the full walkthrough

Core Modules (See more at ARCHITECTURE)

figaroh.calibration — Geometric Calibration

BaseCalibration provides a complete framework for kinematic parameter calibration:

• Automatic parameter identification using QR decomposition
• Robust optimization with iterative outlier removal (Levenberg-Marquardt)
• Unit-aware weighting for position/orientation measurements
• Multiple calibration models: full kinematic parameters, joint offsets
• Sensor support: cameras, motion capture, planar constraints

figaroh.identification — Dynamic Identification

BaseIdentification implements the complete dynamic parameter identification workflow:

• Standard + extended parameters: inertial parameters, friction (viscous/Coulomb), actuator inertia, joint offsets
• Regressor-based identification with base parameter reduction
• Multiple solvers: Least Squares, Weighted LS, Ridge, Lasso, Elastic Net
• Decimation and filtering for signal processing
• Quality metrics: RMSE, correlation, condition number

Physical Consistency (optional, default-off)

FIGAROH can optionally project per-joint inertial parameters onto a physically consistent set using a convex SDP/LMI based on Pinocchio pseudo-inertia.

• Enable it in config via identification.physical_consistency.enabled: true.
• Requires optional dependencies: picos and an SDP solver backend (e.g. cvxopt).

figaroh.optimal — Trajectory & Configuration Optimization

BaseOptimalTrajectory generates exciting trajectories for dynamic identification:

• IPOPT-based nonlinear optimization with cyipopt
• Cubic spline parameterization for C² continuous trajectories
• Constraint handling: joint limits, velocity limits, torque limits, self-collision
• Cost functions: condition number minimization, excitation maximization

BaseOptimalCalibration selects optimal calibration configurations:

• Combinatorial optimization from feasible posture pool
• Observability-based selection for maximum information gain

figaroh.tools — Robotics Utilities

figaroh.utils — Configuration & Results

Methodology

FIGAROH implements a systematic workflow for robot calibration and identification:

Step 1: Configuration Setup

Define robot parameters, sensor configurations, and task-specific settings in YAML:

# config/robot_config.yaml
robot:
  name: "my_robot"
  urdf_path: "models/robot.urdf"

calibration:
  start_frame: "base_link"
  end_frame: "tool0"
  method: "full_params"

identification:
  has_friction: true
  has_actuator_inertia: true
  active_joints: ["joint1", "joint2", "joint3"]

  physical_consistency:
    enabled: false
    solver: "cvxopt"
    mass_min: 1e-6
    psd_eig_tol: -1e-10
    skip_if_feasible: true

Step 2: Optimal Experiment Design

Generate exciting trajectories or calibration postures:

• For identification: Solve IPOPT optimization to find trajectories maximizing regressor condition
• For calibration: Combinatorial selection of postures maximizing observability

Step 3: Data Collection & Processing

initialize() loads and validates experimental data (paths come from the YAML config, e.g. measurement_file):

calibrator = MyCalibration(robot, "config/robot_config.yaml")
calibrator.initialize()

Step 4: Parameter Estimation

Run identification/calibration, then get a quality report, an optional HTML report, and a pass/fail verdict for the same run:

# Calibration
calibrator.solve(html_report=True)      # prints + writes results/calibration_report.html
print(f"RMSE: {calibrator.evaluation_metrics['rmse']:.6f}")
verdict = calibrator.verify()           # pass/fail against quality thresholds

# Identification
identifier.solve(decimate=True, decimation_factor=10, html_report=True)
print(f"Correlation: {identifier.correlation:.4f}")
verdict = identifier.verify()
identifier.export_verification_report()  # results/identification_verification.json

Step 5: Model Update

Export calibrated/identified parameters to URDF or YAML.

Dependencies

Citations

If you use FIGAROH in your research, please cite the following papers:

Main Reference

@inproceedings{nguyen2023figaroh,
  title={FIGAROH: a Python toolbox for dynamic identification and geometric calibration of robots and humans},
  author={Nguyen, Dinh Vinh Thanh and Bonnet, Vincent and Maxime, Sabbah and Gautier, Maxime and Fernbach, Pierre and others},
  booktitle={IEEE-RAS International Conference on Humanoid Robots},
  pages={1--8},
  year={2023},
  address={Austin, TX, United States},
  doi={10.1109/Humanoids57100.2023.10375232},
  url={https://hal.science/hal-04234676v2}
}

Related Work

@inproceedings{nguyen2024improving,
  title={Improving Operational Accuracy of a Mobile Manipulator by Modeling Geometric and Non-Geometric Parameters},
  author={Nguyen, Thanh D. V. and Bonnet, V. and Fernbach, P. and Flayols, T. and Lamiraux, F.},
  booktitle={2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids)},
  pages={965--972},
  year={2024},
  address={Nancy, France},
  doi={10.1109/Humanoids58906.2024.10769790}
}

@techreport{nguyen2025humanoid,
  title={Humanoid Robot Whole-body Geometric Calibration with Embedded Sensors and a Single Plane},
  author={Nguyen, Thanh D V and Bonnet, Vincent and Fernbach, Pierre and Daney, David and Lamiraux, Florent},
  year={2025},
  institution={HAL},
  url={https://hal.science/hal-05169055}
}

License

Please refer to the LICENSE file for licensing information.