Getting Started

Installation

Install FIGAROH from PyPI:

pip install figaroh

For development with all dependencies:

conda env create -f environment.yml
conda activate figaroh-dev
pip install -e .

Configuration System

FIGAROH uses a flexible YAML-based configuration system that supports both modern unified format and legacy format.

Unified Configuration Format

The modern unified format provides better organization and template inheritance:

# modern_config.yaml
inherit_from: "templates/base_robot.yaml"

robot:
  name: "tiago"
  urdf_path: "urdf/tiago.urdf"

calibration:
  method: "full_params"
  sensor_type: "camera"

  markers:
    - ref_joint: "wrist_3_joint"
      position: [0.1, 0.0, 0.05]
      measure: [true, true, true, true, true, true]

identification:
  mechanics:
    friction_coefficients:
      viscous: [0.01, 0.02, 0.015]
      static: [0.001, 0.002, 0.0015]
    actuator_inertias: [0.1, 0.15, 0.12]

  signal_processing:
    sampling_frequency: 5000.0
    cutoff_frequency: 100.0

Legacy Format Support

Existing configurations continue to work without modification:

# legacy_config.yaml
calibration:
  calib_level: full_params
  markers:
    - ref_joint: wrist_3_joint
      measure: [True, True, True, True, True, True]

identification:
  robot_params:
    - fv: [0.01, 0.02, 0.015]
      fs: [0.001, 0.002, 0.0015]
  processing_params:
    - ts: 0.0002
      cut_off_frequency_butterworth: 100.0

Quick Start Examples

Basic Calibration

from figaroh.calibration import BaseCalibration
from figaroh.tools.robot import load_robot

# Load robot and run calibration
robot = load_robot("path/to/robot.urdf")
calibration = BaseCalibration(robot, "config/calibration_config.yaml")
calibration.load_data("data/calibration_data.csv")
results = calibration.run_calibration()

Basic Identification

from figaroh.identification import BaseIdentification
from figaroh.tools.robot import load_robot

# Load robot and run identification
robot = load_robot("path/to/robot.urdf")
identification = BaseIdentification(robot, "config/identification_config.yaml")
identification.load_data("data/identification_data.csv")
params = identification.run_identification()

Advanced Regressor Building

from figaroh.tools.regressor import RegressorBuilder, RegressorConfig

# Configure regressor
config = RegressorConfig(
    has_friction=True,
    has_actuator_inertia=True,
    is_joint_torques=True
)

# Build regressor matrix
builder = RegressorBuilder(robot, config)
W = builder.build_basic_regressor(q, dq, ddq)

Configuration Management

from figaroh.utils.config_parser import UnifiedConfigParser

# Parse any configuration format
parser = UnifiedConfigParser("config/robot_config.yaml")
config = parser.parse()

# Create task-specific configuration
calib_config = parser.create_task_config(robot, config, "calibration")
identif_config = parser.create_task_config(robot, config, "identification")

Next Steps

Explore the Examples Repository for complete workflows
Check out the API documentation for detailed module information
Review the configuration templates for your specific robot type