IR-SIM is an open-source, Python-based, lightweight robot simulator designed for navigation, control, and learning. It provides a simple, user-friendly framework with built-in collision detection for modeling robots, sensors, and environments. Ideal for academic and educational use, IR-SIM enables rapid prototyping of robotics and learning algorithms in custom scenarios with minimal coding and hardware requirements.

• Simulate robot platforms with diverse kinematics, sensors, and behaviors (support).
• Quickly configure and customize scenarios using straightforward YAML files. No complex coding required.
• Visualize simulation outcomes using a naive visualizer matplotlib for immediate debugging.
• Support collision detection and customizable behavior policies for each object.
• Suitable for mutli-agent/robot learning (Projects).

Multi-Robot RVO Collision Avoidance Source	Ackermann Robot with 2D LiDAR Source	HM3D / MatterPort3D Grid Map Source
Field-of-View Detection Source	Dynamic Random Obstacles Source	200-Agent ORCA via pyrvo Source

Requires Python >= 3.10

pip install ir-sim

# Optional: keyboard control and all extras
pip install ir-sim[all]

git clone https://github.com/hanruihua/ir-sim.git
cd ir-sim
pip install -e .

git clone https://github.com/hanruihua/ir-sim.git
cd ir-sim
uv sync

A minimal example: a differential-drive robot navigates toward a goal using the built-in dash behavior.

import irsim

env = irsim.make('robot_world.yaml') # initialize the environment with the configuration file

for i in range(300): # run the simulation for 300 steps

    env.step()  # update the environment
    env.render() # render the environment

    if env.done(): break # check if the simulation is done

env.end() # close the environment

YAML Configuration: robot_world.yaml

world:
  height: 10  # the height of the world
  width: 10   # the width of the world
  step_time: 0.1  # 10Hz calculate each step
  sample_time: 0.1  # 10 Hz for render and data extraction
  offset: [0, 0] # the offset of the world on x and y

robot:
  kinematics: {name: 'diff'}  # omni, diff, acker
  shape: {name: 'circle', radius: 0.2}  # radius
  state: [1, 1, 0]  # x, y, theta
  goal: [9, 9, 0]  # x, y, theta
  behavior: {name: 'dash'} # move toward to the goal directly
  color: 'g' # green

For more examples, see the usage directory and the documentation.

• English: https://ir-sim.readthedocs.io/en
• Chinese (中文): https://ir-sim.readthedocs.io/zh-cn

• [RAL & ICRA 2023] rl-rvo-nav -- Reinforcement learning-based RVO behavior for multi-robot navigation.
• [RAL & IROS 2023] RDA_planner -- Accelerated collision-free motion planner for cluttered environments.
• [T-RO 2025] NeuPAN -- Direct point robot navigation with end-to-end model-based learning.

• DRL-robot-navigation-IR-SIM -- Deep reinforcement learning for robot navigation.
• AutoNavRL -- Autonomous navigation using reinforcement learning.

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

• PythonRobotics

IR-SIM is released under the MIT License.