robometrics 0.1.1

Lightweight robotics metrics for Python.

RoboMetrics

Lightweight robotics metrics for Python.

RoboMetrics is a small local Python library for computing robotics trajectory, prediction, safety, comfort, and physics metrics from NumPy arrays and simple CSV/JSON trajectory files.

Why This Exists

Robotics projects often grow scattered metric functions across notebooks, experiments, and scripts. RoboMetrics keeps common metrics in one typed, dependency-light package that can be imported directly inside local robotics codebases.

Installation

pip install robometrics

For local development:

python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
pytest
ruff check .
mypy robometrics

Quickstart

import numpy as np

from robometrics import ade, fde, path_length

prediction = np.array([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]])
ground_truth = np.array([[0.0, 0.0], [1.1, 0.0], [2.2, 0.0]])

print("ADE (m):", ade(prediction, ground_truth))
print("FDE (m):", fde(prediction, ground_truth))
print("Path length (m):", path_length(ground_truth))

Runnable examples:

python examples/trajectory_metrics.py
python examples/prediction_metrics.py
python examples/safety_metrics.py
python examples/comfort_metrics.py
python examples/load_from_csv.py

Input Shapes

Trajectory-like inputs are NumPy-compatible arrays with finite numeric values:

• Single trajectory: Nx2 or Nx3
• Multimodal predictions: KxTx2 or KxTx3
• Ground-truth trajectory for prediction metrics: Tx2 or Tx3
• Actor trajectories for safety metrics: a list of Nx2 or Nx3 arrays

N or T is the number of timesteps, K is the number of prediction modes, and columns are position coordinates in meters. Nx3 inputs are supported by metrics that operate on positions; distance-based metrics use all available coordinate dimensions unless documented otherwise.

Empty arrays, NaN/inf values, bad ranks, mismatched trajectory lengths, invalid dt, and incompatible dimensions raise ValueError with a targeted message.

Units

• Position and distance inputs: meters
• Time step dt: seconds
• Speed: meters per second
• Acceleration: meters per second squared
• Jerk: meters per second cubed
• Curvature: inverse meters
• Rates and scores: unitless floats

Return Types

Most public metric functions return float or numpy.ndarray. Threshold-style physics helpers return MetricResult, which stores a value, unit, optional threshold, pass/fail status, and metadata.

EvaluationResult is a small container for local batches of metric results and can export dictionaries, strict JSON, Markdown tables, and pandas DataFrames. Non-finite metric values are serialized as null in JSON.

Metric Examples

Trajectory

import numpy as np

from robometrics import curvature, path_length

trajectory = np.array([[0.0, 0.0], [1.0, 0.2], [2.0, 0.5]])

print(path_length(trajectory))  # meters
print(curvature(trajectory))    # 1/meters

Prediction

import numpy as np

from robometrics import min_ade, min_fde, miss_rate

predictions = np.array(
    [
        [[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]],
        [[0.0, 0.0], [1.1, 0.0], [2.1, 0.0]],
    ]
)
ground_truth = np.array([[0.0, 0.0], [1.0, 0.1], [2.0, 0.2]])

print(min_ade(predictions, ground_truth))
print(min_fde(predictions, ground_truth))
print(miss_rate(predictions, ground_truth, threshold=0.5))

Safety

import numpy as np

from robometrics import collision_rate, min_distance_to_actors

ego = np.array([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]])
actors = [np.array([[0.0, 2.0], [1.0, 1.0], [2.0, 0.4]])]

print(min_distance_to_actors(ego, actors))                 # meters
print(collision_rate(ego, actors, ego_radius=0.3, actor_radius=0.3))

Comfort

import numpy as np

from robometrics import acceleration, jerk, jerk_cost

trajectory = np.array([[0.0, 0.0], [1.0, 0.1], [2.0, 0.4], [3.0, 0.9]])
dt = 0.5

print(acceleration(trajectory, dt=dt))  # m/s^2
print(jerk(trajectory, dt=dt))          # m/s^3
print(jerk_cost(trajectory, dt=dt))

Physics

import numpy as np

from robometrics import acceleration_limits_violated, dynamic_feasibility_score

trajectory = np.array([[0.0, 0.0], [1.0, 0.1], [2.0, 0.3], [3.0, 0.6]])

print(acceleration_limits_violated(trajectory, dt=0.5, max_accel=5.0))
print(dynamic_feasibility_score(trajectory, dt=0.5))

CSV And JSON

from robometrics.io import load_trajectory_csv, load_trajectory_json

csv_traj = load_trajectory_csv("trajectory.csv")
json_traj = load_trajectory_json("trajectory.json")

CSV files must include x and y columns, and may include z. JSON files may contain either a raw list of points or an object with a points field.

Lightweight Evaluator And Registry

The evaluator and registry are intentionally small helpers for local scripts. Use them when named metric selection or threshold reporting is useful:

from robometrics import Evaluator, registry

print(registry.list_metrics())

result = Evaluator().evaluate(
    prediction=prediction,
    ground_truth=ground_truth,
    metrics=["ade", "fde"],
    thresholds={"ade": 0.5, "fde": 1.0},
)

print(result.to_json())

Unknown metric names raise UnknownMetricError before evaluation starts. Metric execution failures are returned as failed MetricResult entries with metadata["error"].

Contributing

See CONTRIBUTING.md and docs/contributing.md.

Before opening a pull request:

ruff check .
mypy robometrics
pytest --cov=robometrics --cov-report=term-missing
python examples/trajectory_metrics.py
python examples/prediction_metrics.py
python examples/safety_metrics.py
python examples/comfort_metrics.py
python examples/load_from_csv.py

License

MIT. See LICENSE.