ci-group / revolve2

A python library for optimization, geared towards modular robots and evolutionary computing.
https://ci-group.github.io/revolve2
GNU Lesser General Public License v3.0
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evolutionary-algorithms evolutionary-computation modular-robots optimization robotics simulation

Revolve2

Revolve2 is a collection of Python packages used for researching evolutionary algorithms and modular robotics. Its primary features are a modular robot framework, an abstraction layer around physics simulators, and evolutionary algorithms.

Documentation: ci-group.github.io/revolve2

Installation: ci-group.github.io/revolve2/installation

Get help: github.com/ci-group/revolve2/discussions/categories/ask-for-help

DOI License: LGPL v3 CI

Sample

Here we create and simulate a modular robot, and then calculate how far it moved over the xy plane. This is a shortened version of examples/evaluate_single_robot.

# (...) Omitted preamble

# Create a modular robot.
body = modular_robots_v1.gecko_v1()
brain = BrainCpgNetworkNeighborRandom(body=body, rng=rng)
robot = ModularRobot(body, brain)

# Create a scene.
scene = ModularRobotScene(terrain=terrains.flat())
scene.add_robot(robot)

# Create a simulator.
simulator = LocalSimulator(headless=False)

# Simulate the scene and obtain states sampled during the simulation.
scene_states = simulate_scenes(
    simulator=simulator,
    batch_parameters=make_standard_batch_parameters(),
    scenes=scene,
)

# Get the state at the beginning and end of the simulation.
scene_state_begin = scene_states[0]
scene_state_end = scene_states[-1]

# Retrieve the states of the modular robot.
robot_state_begin = scene_state_begin.get_modular_robot_simulation_state(robot)
robot_state_end = scene_state_end.get_modular_robot_simulation_state(robot)

# Calculate the xy displacement of the robot.
xy_displacement = fitness_functions.xy_displacement(
    robot_state_begin, robot_state_end
)