faran 0.2.4

A library providing performant NumPy & JAX implementations of an MPPI planner, along with implementation of related algorithms/tools.

faran

Primary repository: gitlab.com/risk-metrics/faran — the GitHub mirror exists for Colab notebook support.

Sampling-based trajectory planning for autonomous systems. Provides composable building blocks — dynamics models, cost functions, samplers, and risk metrics — so you can assemble a complete MPPI planner in a few lines and iterate on the parts that matter for your problem.

Installation

pip install faran          # NumPy + JAX (CPU)
pip install faran[cuda]    # JAX with GPU support (Linux)

Requires Python ≥ 3.13.

Quick Start

MPPI planner with MPCC (Model Predictive Contouring Control) for path tracking, using a kinematic bicycle model:

from faran.numpy import mppi, model, sampler, trajectory, types, extract
from numtypes import array

def position(states):
    return types.positions(x=states.positions.x(), y=states.positions.y())

reference = trajectory.waypoints(
    points=array([[0, 0], [10, 0], [20, 5], [30, 5]], shape=(4, 2)),
    path_length=35.0,
)

planner, augmented_model, _, _ = mppi.mpcc(
    model=model.bicycle.dynamical(
        time_step_size=0.1, wheelbase=2.5,
        speed_limits=(0.0, 15.0), steering_limits=(-0.5, 0.5),
        acceleration_limits=(-3.0, 3.0),
    ),
    sampler=sampler.gaussian(
        standard_deviation=array([0.5, 0.2], shape=(2,)),
        rollout_count=256,
        to_batch=types.bicycle.control_input_batch.create, seed=42,
    ),
    reference=reference,
    position_extractor=extract.from_physical(position),
    config={
        "weights": {"contouring": 50.0, "lag": 100.0, "progress": 1000.0},
        "virtual": {"velocity_limits": (0.0, 15.0)},
    },
)

state = types.augmented.state.of(
    physical=types.bicycle.state.create(x=0.0, y=0.0, heading=0.0, speed=0.0),
    virtual=types.simple.state.zeroes(dimension=1),
)
nominal = types.augmented.control_input_sequence.of(
    physical=types.bicycle.control_input_sequence.zeroes(horizon=30),
    virtual=types.simple.control_input_sequence.zeroes(horizon=30, dimension=1),
)

for _ in range(200):
    control = planner.step(temperature=50.0, nominal_input=nominal, initial_state=state)
    state = augmented_model.step(inputs=control.optimal, state=state)
    nominal = control.nominal

To use JAX (GPU), change from faran.numpy to from faran.jax. The API is identical.

Features

See the feature overview for the full list of supported components, backend coverage, and roadmap.

Documentation

Getting Started	Installation, first planner, simulation loop
User Guide	MPPI concepts, cost design, obstacles, boundaries, risk metrics
Examples	Interactive visualizations of MPCC scenarios
API Reference	Factory functions and protocol documentation

Contributing

See CONTRIBUTING.md.

License

MIT — see LICENSE.