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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

[0.4.5] - 2026-07-13

Added

  • feat(identification): Weighted least squares (WLS) support — BaseIdentification.solve(wls=True) refines the OLS base-parameter estimate with iteratively-weighted least squares (Gautier, 1997) before computing quality metrics, so the terminal/HTML report and verify() verdict reflect the WLS-refined parameters rather than a stale OLS estimate. Config-driven via identification.problem.wls in the unified YAML config, following the existing has_friction/has_joint_offset pattern.
  • feat(tooling): figaroh.tools.provenance and figaroh.tools.run_archive — every calibration/identification run now carries a provenance record (git commit, config hash, timestamps, robot/asset identity), and archive_run() writes each run to a timestamped results/runs/<robot>/<task>/<timestamp>/ directory (config snapshot, provenance JSON, report) instead of overwriting a single results/ path.
  • feat(validation): When no separate validation dataset is configured, calibration and identification now fall back to evaluating validation metrics against the training data itself — rather than silently omitting the validation section — with an explicit warning surfaced in the terminal report, HTML report, and machine-readable insights.
  • feat(reporting): Self-contained HTML diagnostic reports for both calibration and identification.
  • figaroh.tools.report.generate_calibration_report() / BaseCalibration.export_html_report() — summary, auto-generated insights, per-DOF residual table, parameter uncertainty bars, correlation section, validation section.
  • figaroh.tools.identification_report.generate_identification_report() / BaseIdentification.export_html_report() — same shape, adapted to identification's per-joint torque residuals and base-parameter uncertainty.
  • figaroh.tools._report_common — shared HTML/CSS kernel (styling, escaping, insight/uncertainty/correlation section builders) reused by both generators.
  • Opt-in via solve(html_report=True) on both base classes, matching the existing plotting/save_results flag pattern.
  • feat(identification): BaseIdentification.print_quality_report() — terminal quality report (condition number, RMSE, per-joint residuals, base-parameter uncertainty, validation, physical-consistency/ reconstruction status), the identification analogue of calibration's existing terminal report.
  • feat(identification): Held-out validation support for identification, mirroring calibration's — a genuinely separate dataset via identification.data.validation_data_file, never a runtime split of training data.
  • feat(reporting): Machine-readable verification verdicts.
  • VerificationVerdict / ThresholdCheck (figaroh.tools._report_common) — a structured pass/fail against per-metric thresholds, with provenance (git commit, config hash, timestamp).
  • verify(thresholds=None) and export_verification_report(output_path=None, output_dir="results", thresholds=None) added to both BaseCalibration and BaseIdentification. Default thresholds cover condition number and, when validation data is configured, validation-set RMSE/correlation/ improvement — every threshold is overridable per call. A threshold whose metric isn't computable is skipped, not failed.
  • Called explicitly (not from inside solve()) — verification is opt-in output computed from already-stored results.
  • feat(reporting): Interactive before/after panel embedded in both HTML reports — a zoomable, hoverable overlay of nominal vs. fitted vs. measured values, rendered as hand-rolled inline SVG (no new dependency).
  • feat(reporting): figaroh.tools.compare_report.generate_compare_page() — a static, self-contained, offline HTML page that loads two exported verification JSON files client-side and renders a per-metric diff table plus an overlaid before/after chart, gated by a mandatory compatibility check (domain, joint/DOF names, decimate, sample count) with an explicit "compare anyway" override on mismatch. No backend, no run history.
  • fix(results_manager): plot_identification_results() reshaped 1D torque arrays as reshape(-1, 1), silently collapsing a multi-joint, joint-major-flattened array into a single mislabeled trace. Now accepts explicit n_joints/joint_names and reshapes joint-major (reshape(n_joints, -1).T) when provided.
  • refactor(results_manager): plot_with_fallback() helper deduplicates the four near-identical try/except plotting blocks across BaseCalibration, BaseIdentification, BaseOptimalCalibration, and BaseOptimalTrajectory.

Fixed

  • fix(calibration): BaseCalibration.calc_stddev() computed the calibrated-parameter count (self.nvars) once in __init__, before initialize() populated the actual parameter list — leaving it stuck at 0 and silently producing empty std_dev/std_pctg lists. The "Parameter uncertainty" section was therefore always empty in both the terminal and HTML calibration reports. Now derives the count from the solved parameter vector (len(result.x)) at computation time.
  • fix(identification): _compute_validation_metrics()'s before/after torque chart sliced the first n_active joint-blocks off the full-model regressor, which is only correct when the active/identified joints are exactly DOFs 0..n_active-1. For robots identifying a strict subset of joints (e.g. TIAGo: torso+arm out of many more DOFs), this silently returned the wrong joints' nominal/identified torque predictions — visible as identical nominal/fitted curves and wildly wrong magnitudes in the before/after chart. Now indexes by the actual act_idxv DOF indices, mirroring the row-selection already used correctly in _decimate_regressor_matrix.
  • fix(identification): _apply_weighted_least_squares() sliced the stacked regressor into model.nv equal blocks to estimate per-joint residual variance, which is only correct when every model joint is identified. For subset-identified robots (TIAGo: model.nv=24, 8 joints identified), this misaligned the slicing entirely, producing an RMSE ~2000x worse than plain OLS. Now uses len(identif_config["act_idxv"]), the same quantity _decimate_regressor_matrix already uses.
  • fix(identification): _prepare_undecimated_data() flattened torque data sample-major while regressor rows are joint-major, corrupting solve(decimate=False)'s correlation/RMSE (0.44 vs 0.966 on identical data pre-fix).
  • fix(reporting): the before/after HTML panel's joint/DOF dropdown rendered its options twice — once server-side in the generated HTML, once again client-side via JS — so every entry appeared duplicated in the selector.
  • fix(reporting): the before/after HTML panel embedded the full validation-set series inline with no cap; for large validation sets (e.g. a fallback-to-full-training-data run with ~45k samples) this produced multi-MB HTML pages whose SVG path strings were too large to render reliably in the browser. Now uniformly subsampled to at most 3000 points, with the displayed sample count disclosed in the chart caption.
  • fix(export_validation): the interactive viser GUI's speed/pose/opacity sliders crashed with AttributeError: 'GuiEvent' object has no attribute 'value' on every drag — GuiEvent exposes the changed control via .target, not .value directly.

Full design history and rationale: docs/decisions/roadmap-mujoco-sysid-inspired-features.md.

[0.4.4] - 2026-06-27

Added

  • feat(calibration): Add validation data support for ground-truth FK testing.
  • _load_validation_data(path) — load a separate validation measurement CSV.
  • _compute_validation_metrics() — compare nominal vs calibrated FK against held-out measurements; reports position/orientation RMSE, max error, and improvement percentage.
  • Validation data path configurable via YAML (validation_data_file) or CLI (--validation-data) in example scripts.
  • feat(calibration): Add comprehensive statistical quality report.
  • print_quality_report() — formatted terminal table with convergence status, per-DOF residuals (X/Y/Z/rx/ry/rz with R²), overall RMSE, validation metrics, parameter uncertainty (top 5), and strongly correlated pairs (|ρ| > 0.8).
  • _compute_per_dof_stats() — mean, std, RMSE, max, R² per kinematic DOF.
  • _compute_condition_number() — condition number of Jacobian with well/moderate/ill-conditioned label.
  • _compute_parameter_correlation() — flags parameter pairs with |ρ| > 0.8 from the covariance matrix.
  • Report printed automatically after solve() completes.
  • feat(calibration): Add position_frame='body'|'world' option to _compute_logmap_residuals() — choose body-frame or world-frame position error.
  • feat(urdf_exporter): New module figaroh.tools.urdf_exporter for applying calibrated joint parameters to URDF files with 12 joint-level categories.
  • feat(export_validation): New module figaroh.tools.export_validation with URDFComparison class — FK consistency checks, interactive viser visualization (trajectory animation, static overlay, error plots, replay controls).
  • feat(backends): Multi-simulator backend architecture.
  • PinocchioBackend — 371 lines wrapping all Pinocchio dynamics/kinematics calls.
  • MuJoCoBackend — 441 lines with URDF→MJCF auto-conversion.
  • DynamicsBackend abstract interface with 9 abstract + 6 optional methods.
  • RobotIdentificationSystem high-level integration API.
  • feat(tests): Cross-backend validation suite, backend unit tests (32 Pinocchio
  • 13 MuJoCo), TIAGo integration test fixture, URDF exporter tests.
  • docs: Combined ROADMAP.md (Track A + Track B), comprehensive architecture docs, ADR for calibration validation plan.

Fixed

  • fix(calibration): Replace element-wise RPY subtraction with proper SE3 log-map pose error in _compute_logmap_residuals() — geometrically correct orientation residuals without singularity issues.
  • fix(backends): MuJoCoBackend regressor (delegates to Pinocchio analytical), Coriolis matrix (finite-difference Jacobian/2), mass matrix (adds missing mj_forward call).

Changed

  • breaking(export_validation): Rename trajectory_errors() → fk_consistency_check() and TrajectoryErrors → FkConsistencyResult — clarifies this is a file-integrity check, NOT ground-truth FK validation.
  • chore: Add black formatter to pre-commit hooks.
  • chore: Modernize pyproject.toml with dev dependencies and tool configs.

Tests

  • 293 passed, 20 skipped, 0 failed (from 208 passed with 4 pre-existing failures).
  • New: test_backends.py (45 tests), test_cross_backend.py (integration), test_tiago_fixture.py (287 lines), test_urdf_exporter.py (296 lines), test_integration.py (250 lines).

[0.4.3] - 2026-06-02

Added

  • feat(cad_constraints): New module figaroh.identification.cad_constraints with convex CAD-informed constraints for inertial parameter identification.
  • CADConstraints dataclass — container for per-link mass bounds, first-moment (CoM) bounds, and symmetry equality constraints.
  • add_mass_bounds(cad, joint, *, m_min, m_max) — add box constraint m_min ≤ m_j ≤ m_max for a link.
  • add_com_bounds(cad, joint, *, axis, h_min, h_max) — add first-moment (linear) box constraint h_min ≤ h_k ≤ h_max for axis k ∈ {x,y,z}.
  • add_symmetry_constraints(cad, joint_a, joint_b, *, keys) — add equality constraints between inertial parameters of two symmetric links.
  • bounds_from_urdf(model, ...) — derive mass and CoM bounds automatically from Pinocchio model URDF inertials (CAD data source strategy).
  • build_cad_constraints_from_config(cfg, *, model) — build CADConstraints from a YAML config sub-dict; returns None when empty (safe default-off).
  • apply_cad_constraints_to_problem(problem, theta, params_r, cad) — inject CAD constraints into a picos Problem for the full-robot SDP.
  • feat(physical_consistency): project_p10_lmi gains optional mass_bounds and com_bounds kwargs — per-link box constraints on the SDP.
  • feat(physical_consistency): project_robot_p10_lmi gains optional cad_constraints kwarg — passes per-link bounds to project_p10_lmi.
  • feat(reconstruction): _reconstruct_sdp and reconstruct_full_parameters gain optional cad_constraints kwarg — injects CAD constraints after the per-joint LMI loop in the full-robot SDP.
  • feat(base_identification): Both _apply_physical_consistency_if_enabled and _apply_reconstruction_if_enabled hooks parse cad_constraints from config and pass the result to the underlying solvers.
  • Example YAML comment block for cad_constraints in figaroh-examples/examples/templates/manipulator_robot.yaml.

Changed

  • All new parameters are optional with safe defaults — fully backward-compatible.

[0.4.2] - Unreleased

Added

  • feat(reconstruction): ReconstructionResult.status — outcome code ("ok", "solver_missing", "error") on every result
  • feat(reconstruction): ReconstructionResult.base_residual_norm — L2 norm of the base constraint residual M θ − φ_base (scalar, always set)
  • feat(reconstruction): ReconstructionResult.objective — SDP objective value for Option B (None for nullspace)
  • feat(reconstruction): BaseResult frozen dataclass — structured container for (M, phi_base, params_r) as input to reconstruct_full_parameters()
  • feat(reconstruction): reconstruct_full_parameters() — unified entry point with method="nullspace" | "sdp" | "auto" dispatch
  • feat(reconstruction): Option B SDP (method="sdp") — minimises ‖diag(w)(θ−θ₀)‖² subject to M θ = φ_base and per-joint pseudo-inertia LMI P_j ≽ 0 via Schur-complement epigraph (requires picos + cvxopt/mosek)
  • feat(reconstruction): method="auto" — silently falls back to nullspace when picos is not installed; status="solver_missing" when picos unavailable and method="sdp" was requested explicitly
  • feat(reconstruction): _load_prior_from_urdf() — builds flat prior dict from Pinocchio model inertias (prior_source="urdf")
  • feat(reconstruction): _load_prior_from_yaml() — loads prior from a flat YAML file (prior_source="yaml")
  • feat(identification): _apply_reconstruction_if_enabled() hook in BaseIdentification._store_results() — called after physical consistency; stores result under self.result["reconstruction"]
  • feat(identification): _calculate_base_parameters() now uses QRDecomposer directly to expose self._M_matrix / self._params_r_for_recon for downstream reconstruction; result dict includes "M" and "params_r" keys
  • feat(config): reconstruction block parsed from both legacy YAML (get_param_from_yaml) and unified config (unified_to_legacy_identif_config)
  • feat(identification/__init__): BaseResult and reconstruct_full_parameters added to public exports and __all__
  • tests: 13 new unit tests in tests/unit/test_reconstruction.py covering new fields, BaseResult, prior helpers, _p10_indices_for_joints, nullspace end-to-end, auto fallback, YAML prior, and unsupported method error

Fixed

  • fix(reconstruction): alternation loop in run_reconstruction now correctly unpacks project_robot_p10_lmi() as (projected_p10_dict, robot_report); removes AttributeError: tuple has no attribute 'p10_by_joint'
  • fix(identification): "projected parameters" key (space) renamed to "projected_parameters" (underscore) in _apply_physical_consistency_if_enabled result dict for consistency with "raw_parameters" and Python conventions
  • fix(tools/robotcollisions): print_collision_pairs() uses print() instead of logger.info() so output is visible in interactive use and captured by tests
  • fix(tools/solver): LinearSolver._print_solution_info() uses print() instead of logger.info() so verbose output is visible in interactive use and captured by tests

[0.4.1] - Unreleased

Added

  • feat(physical-consistency): is_feasible_link() — public alias for check_p10_feasibility() matching roadmap spec naming
  • feat(physical-consistency): project_link() — public alias for project_p10_lmi() matching roadmap spec naming
  • feat(physical-consistency): ProjectionReport.runtime — per-link solve time (seconds) recorded via time.perf_counter() around problem.solve()
  • feat(physical-consistency): weights keyword argument to project_robot_p10_lmi() for passing a 10-element weight vector to all per-link projection calls
  • feat(config): weights.mode: "auto" | "manual" parsed from the physical_consistency YAML block in _apply_physical_consistency_if_enabled
  • feat(config): weights.manual.{m, h, Sigma} per-group manual weights built into a 10-element array and forwarded to project_robot_p10_lmi
  • feat(identification): physical consistency result dict now stores both raw_parameters (pre-projection) and projected_parameters (post-projection) as separate keys, preserving the original identified values for comparison
  • tests: 28 unit tests in tests/unit/test_physical_consistency.py covering TC-1 through TC-12 (projection correctness, aliases, runtime, robot aggregation) plus 3 config-wiring tests for weights and raw/projected separation

Fixed

  • fix(physical-consistency): SDP formulation in project_p10_lmi no longer uses the picos 2.x-incompatible pc.vstack, pc.multiply, or pc.sum_squares functions; replaced with element-wise objective using pc.SquaredNorm and explicit sigma-entry loop
  • fix(physical-consistency): solver keyword verbose= replaced with verbosity= (picos 2.x API); eliminates DeprecationWarning on every call
  • fix(physical-consistency): feasibility check after projection uses a relaxed psd_eig_tol=-1e-8 tolerance to absorb inevitable SDP solver numerical noise, preventing valid projections from being reported as "infeasible" due to tiny eigenvalue violations (~1e-9)

[0.3.1] - 2026-04-01

Added

  • feat(physical-consistency): optional projection of inertial parameters onto a physically consistent set (identification/physical_consistency.py)
  • feat(reconstruction): reconstruction utilities — run_reconstruction and run_option_a_reconstruction entry points (identification/reconstruction.py)
  • feat(qrdecomposition): get_M / get_M_labels methods for retrieving the stored base mapping matrix after decomposition
  • feat(qrdecomposition): QRResult dataclass for structured, full-precision decomposition output (rank, base_indices, W_b, beta, M, phi_b, diag_R, cond_R1, method, …)
  • feat(qrdecomposition): relative_tolerance constructor parameter for scale-invariant rank detection (threshold relative to largest pivot)
  • feat(qrdecomposition): get_diagnostics() method returning rank, diag_R, cond_R1, and method after every decomposition
  • feat(qrdecomposition): decompose() unified entry point returning a QRResult; delegates to pivoting or double path based on method argument
  • feat(identification): enhanced parameter management with standard and custom parameter support in BaseIdentification

Fixed

  • fix(qrdecomposition): _find_rank now correctly returns 0 for zero/empty matrices (previously returned row count, causing phantom base parameters)
  • fix(reconstruction): add missing run_option_a_reconstruction alias that blocked package-level import

Changed

  • refactor(qrdecomposition): replace non-pivoted numpy.linalg.qr with scipy.linalg.qr(..., pivoting=True) in _identify_base_parameters — permutation-stable, deterministic basis selection
  • refactor(qrdecomposition): remove premature np.around from beta in all code paths; rounding deferred to display-only _build_parameter_expressions
  • refactor(qrdecomposition): remove fragile assert np.allclose(W_base, W_b)
  • refactor(qrdecomposition): enhanced docstrings and nominal-parameter handling
  • refactor(identification): patch filter configuration management in BaseIdentification
  • refactor(calibration): update deprecated Frame.parent → Frame.parentJoint for Pinocchio 3.x compatibility
  • refactor(logging): replace print statements with logging calls across calibration and identification modules
  • refactor(config): rename load_from_yaml → load_param for clarity

Tests

  • test(qrdecomposition): TestNumericalImprovements suite — 14 new tests, 23/23 total pass (rank-zero, relative tolerance, permutation stability ×20, mapping matrix property, column space, diagnostics, QRResult structure, full-precision beta)
  • test(reconstruction): unit tests for reconstruction utilities

[0.3.0] - 2025-12-09

Added

  • Advanced Linear Solver (figaroh.tools.solver): Comprehensive multivariate linear solver for robot parameter identification
  • Multiple solving methods: lstsq, QR, SVD, Ridge, Lasso, Elastic Net, Tikhonov, constrained, robust, weighted
  • Regularization support: L1 (Lasso), L2 (Ridge), Elastic Net, custom Tikhonov
  • Constraint handling: Box constraints (bounds), linear equality/inequality constraints
  • Robust regression with iterative reweighting for outlier resistance
  • Comprehensive solution quality metrics (RMSE, R², condition number, residuals)
  • Optimized for dense, large, thin matrices typical in robot dynamics

  • Full unit test coverage (18 tests) with robot identification scenarios

  • Module Reorganization: Better code organization and separation of concerns

  • Calibration module restructuring:
    • calibration/config.py: Configuration parsing and YAML handling (624 lines)
    • calibration/parameter.py: Parameter management utilities (240 lines)
    • calibration/data_loader.py: Data loading and I/O operations (160 lines)
  • Identification module restructuring:
    • identification/config.py: Configuration parsing for identification (334 lines)
    • identification/parameter.py: Parameter management for identification (388 lines)

  • Maintains 100% backward compatibility through re-exports

  • BaseIdentification Enhancement:

  • solve_with_custom_solver(): New method using advanced linear solver with regularization and constraints
  • Flexible solving with multiple methods and custom constraints
  • Support for physical parameter bounds (e.g., positive masses/inertias)

Improved

  • Parameter Ordering: Changed to Pinocchio dynamic parameter ordering for consistency
  • New order: [Ixx, Ixy, Ixz, Iyy, Iyz, Izz, mx, my, mz, m]

  • Previous order: [m, mx, my, mz, Ixx, Ixy, Iyy, Ixz, Iyz, Izz]

  • Regressor Module: Cleaned up build_basic_regressor methods

  • Removed unused tau parameter for better API clarity

  • Improved method signatures and documentation

  • Code Quality: Significant reduction in code duplication

  • calibration_tools.py: Reduced from ~1500 to ~630 lines (-58%)
  • identification_tools.py: Reduced from ~900 to ~295 lines (-67%)
  • Modular design with clear single responsibilities

Fixed

  • Parameter naming: Changed from numbered indices to parent joint names for clarity
  • Regressor handling: Better support for additional columns in regressor matrices
  • Results manager imports and formatting issues

Technical Details

  • Files Changed: 21 files
  • Lines Added: +3,372
  • Lines Removed: -1,604
  • Net Change: +1,768 lines
  • Test Coverage: All 18 new solver tests passing

[0.2.4] - 2025-09-08

Changed

  • Optional Dependencies: Removed cyipopt from required dependencies
  • cyipopt is now truly optional and loaded only when IPOPT optimization is used
  • Users can install without cyipopt and still use all other features
  • Install cyipopt separately when needed: pip install cyipopt or via conda environment

Improved

  • Installation Flexibility: Package now installs without requiring heavy optimization dependencies
  • Error Handling: Better error messages when optional dependencies are missing

[0.2.3] - 2025-09-08

Added

  • Streamlined Dependencies: All core dependencies now available via PyPI with automatic installation
  • Lazy Loading: Optional dependencies (cyipopt) now loaded only when needed to improve startup time
  • Enhanced Installation Notes: Clear documentation of simplified dependency management

Improved

  • Dependency Management: Complete cleanup and optimization of package dependencies
  • Removed redundant requirements.txt and setup.py files
  • Consolidated all dependencies in pyproject.toml
  • Updated to use PyPI versions of robotics libraries (pin for Pinocchio)
  • Installation Process: Significantly simplified installation with better cross-platform compatibility
  • Documentation: Comprehensive README updates reflecting modern packaging standards
  • Combined development installation methods for clarity
  • Added official Pinocchio repository reference
  • Updated dependency documentation with descriptions
  • Performance: Faster module loading through localized imports
  • Environment Setup: Streamlined conda environment with minimal dependencies

Enhanced

  • Import Strategy: Localized cyipopt import to specific functions for better error handling
  • Error Messages: More informative import error messages with installation instructions
  • Package Structure: Modern Python packaging standards with pyproject.toml-only approach

Removed

  • Redundant Files: Eliminated requirements.txt and setup.py in favor of modern pyproject.toml
  • Unnecessary Dependencies: Cleaned up unused dependencies for leaner installation

Fixed

  • Package Name: Corrected dependency references (e.g., proper use of pin for Pinocchio PyPI version)
  • Installation Conflicts: Resolved potential conflicts between conda and pip installations

[0.2.0] - 2025-09-05

Added

  • Unified Configuration System: Complete overhaul of configuration management
  • New UnifiedConfigParser with YAML template inheritance
  • Automatic format detection for seamless legacy compatibility
  • Advanced parameter mapping between configuration formats

  • Comprehensive configuration validation with helpful error messages

  • Enhanced Base Classes: Modern object-oriented workflow management

  • BaseCalibration: Standardized calibration workflow with unified config support
  • BaseIdentification: Standardized identification workflow with unified config support

  • Automatic configuration format detection and conversion

  • Advanced Regressor Builder: Complete redesign of regressor computation

  • RegressorBuilder: Object-oriented, extensible regressor construction
  • RegressorConfig: Configuration dataclass for regressor parameters
  • Enhanced input validation and error handling

  • Support for joint torque and external wrench modes

  • Configuration Format Mapping: Seamless format conversion utilities

  • unified_to_legacy_config: Calibration parameter mapping function
  • unified_to_legacy_identif_config: Identification parameter mapping function
  • Perfect compatibility with existing legacy configurations

Improved

  • Parameter Processing: Enhanced parameter handling with better defaults
  • Error Messages: More informative validation and error reporting
  • Documentation: Comprehensive updates to README and module documentation
  • Code Organization: Better structured modules with clear responsibilities
  • Type Safety: Added type hints throughout the codebase

Enhanced

  • Cross-Platform Support: Improved compatibility across operating systems
  • Input Validation: Robust parameter validation and type checking
  • Template System: Flexible configuration template inheritance
  • Backward Compatibility: Full support for existing legacy configurations

Removed

  • quadprog dependency: Removed unused quadprog dependency to reduce package size

Fixed

  • Configuration Parsing: Resolved edge cases in YAML parsing
  • Parameter Mapping: Accurate conversion between configuration formats
  • Validation Logic: Improved configuration validation accuracy
  • Error Handling: Better error recovery and user feedback

Technical Improvements

  • Modern Python practices with dataclasses and type hints
  • Enhanced error handling with custom exception classes
  • Improved testing framework with comprehensive validation
  • Better code documentation and examples

Documentation

  • Updated README with modern API examples
  • Enhanced configuration system documentation
  • New API usage patterns and best practices
  • Comprehensive module documentation updates

[0.1.0] - 2025-01-25

Added

  • Initial release of FIGAROH package
  • Dynamic identification algorithms for rigid multi-body systems
  • Geometric calibration algorithms for serial and tree-structure robots
  • Support for URDF modeling convention
  • Optimal trajectory generation for dynamic identification
  • Optimal posture generation for geometric calibration
  • Integration with Pinocchio for efficient computations
  • Support for various optimization algorithms
  • Data filtering and pre-processing utilities
  • Model parameter update utilities

Features

  • Dynamic Identification:
  • Dynamic model including friction, actuator inertia, and joint torque offset
  • Continuous optimal exciting trajectory generation
  • Multiple parameter estimation algorithms

  • Physically consistent standard inertial parameters calculation

  • Geometric Calibration:

  • Full kinematic parameter calibration
  • Optimal calibration posture generation via combinatorial optimization
  • Support for external sensors (cameras, motion capture)
  • Non-external methods (planar constraints)

Dependencies

  • Core scientific computing: numpy, scipy, matplotlib, pandas
  • Robotics: pinocchio (via conda)
  • Optimization: cyipopt (via conda)
  • Visualization: meshcat
  • Additional: numdifftools, ndcurves, rospkg

Documentation

  • Comprehensive README with installation and usage instructions
  • Examples moved to separate repository (figaroh-examples)
  • API documentation structure prepared

Notes

  • Examples and URDF models moved to separate repository for clean package distribution
  • Package optimized for PyPI distribution
  • Supports Python 3.8+