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: 0.6.0

For contributors: Roadmap, Architecture, Contribution workflow, and validation guide.

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

Working with an AI coding agent? skills/ holds agent skills that set up a FIGAROH task end to end — start with skills/figaroh-start, which routes to the right package and directory, and skills/figaroh-setup-env, which ships a doctor.sh that verifies your environment before anything runs.

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 3.7 and 4.1 compatibility checks
• 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

The Pinocchio compatibility matrix pins 3.7.0 with ndcurves 2.0.0.1 and 4.1.0 with ndcurves 2.3.0 on Python 3.12. Install Pinocchio and ndcurves together so their native dependencies agree. Other versions inside the declared range are eligible for dependency resolution but have no separate CI profile. See the version support decision and validation guide.

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.