https://github.com/ci-group/revolve2

A python library for optimization, geared towards modular robots and evolutionary computing.
https://github.com/ci-group/revolve2

evolutionary-algorithms evolutionary-computation modular-robots optimization robotics simulation

Last synced: 8 days ago
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A python library for optimization, geared towards modular robots and evolutionary computing.

• Host: GitHub
• URL: https://github.com/ci-group/revolve2
• Owner: ci-group
• License: lgpl-3.0
• Created: 2020-07-07T07:54:34.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2025-05-09T15:29:38.000Z (9 months ago)
• Last Synced: 2025-11-30T16:22:32.257Z (about 2 months ago)
• Topics: evolutionary-algorithms, evolutionary-computation, modular-robots, optimization, robotics, simulation
• Language: Python
• Homepage: https://ci-group.github.io/revolve2
• Size: 37.6 MB
• Stars: 23
• Watchers: 2
• Forks: 61
• Open Issues: 43
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

          


# 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](https://ci-group.github.io/revolve2)**

**Installation: [ci-group.github.io/revolve2/installation](https://ci-group.github.io/revolve2/installation)**

**Get help: [github.com/ci-group/revolve2/discussions/categories/ask-for-help](https://github.com/ci-group/revolve2/discussions/categories/ask-for-help)**

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8355869.svg)](https://doi.org/10.5281/zenodo.8355869) [![License: LGPL v3](https://img.shields.io/badge/License-LGPL_v3-blue.svg)](./LICENSE) [![CI](https://github.com/ci-group/revolve2/actions/workflows/main.yml/badge.svg)](https://github.com/ci-group/revolve2/actions)

## 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`.

```python

# (...) 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

)

```