ir-sim 2.11.0

IR-SIM is an open-source, lightweight robot simulator based on Python, designed for robotics navigation, control, and learning. This simulator provides a simple and user-friendly framework for simulating robots, sensors, and environments, facilitating the development and testing of robotics algorithms with minimal hardware requirements.

pip install ir-sim==2.11.0

A lightweight, YAML-driven robot simulator for navigation, control, and learning

Overview
Demonstrations
Installation
Quick Start
Support
Projects Using IR-SIM
Citation

Overview

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.

Key Features

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 results with a lightweight Matplotlib-based renderer for rapid debugging.
Support collision detection and customizable behavior policies for each object.
Suitable for multi-agent and robot-learning research (Projects).

Demonstrations

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

Installation

Requires Python >= 3.10

pip

pip install ir-sim

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

From source

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

uv

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

Quick Start

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, omni_angular, 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.

Support

Category	Features
Kinematics	Differential Drive mobile Robot · Omnidirectional mobile Robot · Omnidirectional with Angular control · Ackermann Steering mobile Robot
Sensors	2D LiDAR · 2D FMCW LiDAR · FOV Detector
Geometries	Circle · Rectangle · Polygon · LineString · Binary Grid Map · Fog of Map
Behaviors	dash (move directly toward goal) · RVO (Reciprocal Velocity Obstacle) · ORCA (Optimal Reciprocal Collision Avoidance) · SFM (Social Force Model)

Documentation

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

Projects Using IR-SIM

Academic Publications

[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.
[ROBIO 2025] MfNeuPAN -- Proactive end-to-end navigation in dynamic environments using direct multi-frame point constraints.
[WCL 2025] Robotic Sensor Network: Achieving Mutual Communication Control Assistance With Fast Cross-Layer Optimization -- Cross-layer communication and control optimization evaluated on IR-SIM.
[IROS 2026] SDLW -- Decentralized scalable exploration using sensor-driven Lévy walks for minimal-sensing robot teams.
[Sensors 2026] PPO-GAT-Follow -- Graph-attention reinforcement learning for robot person following in dense crowds.
[TWC 2026] Energy-Efficient Federated Edge Learning for Small-Scale Datasets in Large IoT Networks -- Energy-efficient federated edge learning evaluated through IR-SIM autonomous-navigation experiments and CARLA validation.

Community Projects

DRL-robot-navigation-IR-SIM -- Deep reinforcement learning for robot navigation.
AutoNavRL -- Autonomous navigation using reinforcement learning.
IRSIM-3DGS-Bridge -- A closed-loop bridge from 3D Gaussian Splatting scenes to IR-SIM planning/following and back to Habitat-GS trajectory playback.
EdgeVox -- Offline voice-agent framework with an IR-SIM mobile-navigation backend.

If your publication or project uses IR-SIM, we welcome proposals for inclusion via an issue or pull request.

Citation

If you find IR-SIM useful, please consider starring ⭐ this project and citing our paper:

@article{han2026ir,
  title={IR-SIM: A Lightweight Skill-Native Simulator for Navigation, Learning, and Benchmarking},
  author={Han, Ruihua and Wang, Shuai and Li, Chengyang and Gao, Rui and Wang, Xinyi and Liu, Zhe and Li, Guoliang and Lu, Yupu and Hao, Qi and Pan, Jia and Zhao, Hengshuang},
  journal={arXiv preprint arXiv:2606.08729},
  year={2026},
  doi={10.48550/arXiv.2606.08729},
  url={https://arxiv.org/abs/2606.08729},
  eprint={2606.08729},
  archivePrefix={arXiv},
  primaryClass={cs.RO}
}

Contributing

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

Acknowledgement

PythonRobotics

License

IR-SIM is released under the MIT License.

Metadata

Release files for ir-sim 2.11.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for ir-sim 2.11.0
File	Size	Uploaded
ir_sim-2.11.0.tar.gz	845.7 kB	Sep 1, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for ir-sim 2.11.0
File	Interpreter	ABI	Platform	Reset
ir_sim-2.11.0-py3-none-any.whl 774.0 kB Sep 1, 2026	Python 3	none	any	Details

Total release size: 1.6 MB

Release files / ir_sim-2.11.0.tar.gz

Download URL	ir_sim-2.11.0.tar.gz
Size	845.7 kB
Tags	Source
SHA-256 checksum How to use checksums	`e7ae6c92c2a8e73bf854abcd64dc4246e20f0716d883bb3b92b4230b2f1ac0aa`
BLAKE2b-256 checksum How to use checksums	`e0beea32a44f91e812bca78725910c802e9210563d116682cb24a13457716381`
Upload date	Sep 1, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.9.25`

Release files / ir_sim-2.11.0-py3-none-any.whl

Download URL	ir_sim-2.11.0-py3-none-any.whl
Size	774.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`4dac9d24e48fef4cc80c86444fb47325979353a9221632e13215ed716350b0fe`
BLAKE2b-256 checksum How to use checksums	`497d9fdb73090463fdd117143c23ff804601b3627627d0089223a86eeda59bef`
Upload date	Sep 1, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.9.25`