{"repo":"hanruihua/ir-sim","free":true,"listed":false,"github":"https://github.com/hanruihua/ir-sim","clone":"git clone https://github.com/hanruihua/ir-sim.git","description":"A Python-based lightweight robot simulator designed for navigation, control, and learning","language":"Python","stars":1112,"topics":["autonomous-driving","control","ir-sim","multi-agent","navigation","path-planning","python","reinforcement-learning","robotics","robotics-simulation","simulator"],"license":"MIT","category":"robotics_simulator","readme_excerpt":"
\n\n\n \n \n \"IR-SIM\n\n\n*A lightweight, YAML-driven robot simulator for navigation, control, and learning*\n\n\"arXiv\n\"Cite\n\"PyPI\n\"Python\n\"CI\"\n\"Coverage\"\n\"Docs\"\n\"License\"\n\"Downloads\"\n\"Ranked\n\n
\n\n## Overview\n\n**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.\n\n## Key Features\n\n- Simulate robot platforms with diverse kinematics, sensors, and behaviors ([support](#support)). \n- Quickly configure and customize scenarios using straightforward YAML files. No complex coding required.\n- Visualize simulation outcomes using a naive visualizer matplotlib for immediate debugging.\n- Support collision detection and customizable behavior policies for each object.\n- Suitable for mutli-agent/robot learning ([Projects](#projects-using-ir-sim)).\n\n## Demonstrations\n\n\n\n\n\n\n\n\n\n\n\n\n
\n
\nMulti-Robot RVO Collision Avoidance
\nSource\n		\n
\nAckermann Robot with 2D LiDAR
\nSource\n		\n
\nHM3D / MatterPort3D Grid Map
\nSource\n
\n
\nField-of-View Detection
\nSource\n		\n
\nDynamic Random Obstacles
\nSource\n		\n
\n200-Agent ORCA via pyrvo
\nSource\n
\n\n## Installation\n\n> **Requires Python >= 3.10**\n\n### pip\n\n```bash\npip install ir-sim\n\n# Optional: keyboard control and all extras\npip install ir-sim[all]\n```\n\n### From source\n\n```bash\ngit clone https://github.com/hanruihua/ir-sim.git\ncd ir-sim\npip install -e .\n```\n\n### uv\n\n```bash\ngit clone https://github.com/hanruihua/ir-sim.git\ncd ir-sim\nuv sync\n```\n\n## Quick Start\n\nA minimal example: a differential-drive robot navigates toward a goal using the built-in `dash` behavior.\n\n```python\nimport irsim\n\nenv = irsim.make(\n \"robot_world.yaml\"\n) # initialize the environment with the configuration file\n\nfor i in range(300): # run the simulation for 300 steps\n env.step() # update the environment\n env.render() # render the environment\n\n if env.done():\n break # check if the simulation is done\n\nenv.end() # close the environment\n```\n\nYAML Configuration: `robot_world.yaml`\n\n```yaml\nworld:\n height: 10 # the height of the world\n width: 10 # the width of the world\n step_time: 0.1 # 10Hz calculate each step\n sample_time: 0.1 # 10 Hz for render and data extraction\n offset: [0, 0] # the offset of the world on x and y\n\nrobot:\n kinematics: {name: 'diff'} # omni, omni_angular, diff, acker\n shape: {name: 'circle', radius: 0.2} # radius\n state: [1, 1, 0] # x, y, theta\n goal: [9, 9, 0] # x, y, theta\n behavior: {name: 'dash'} # move toward to the goal directly\n color: 'g' # green\n```\n\nFor more examples, see the [usage directory](https://github.com/hanruihua/ir-sim/tree/main/usage) and the [documentation](https://ir-sim.readthedocs.io/en).\n\n## Support\n\n| **Category** | **Features** |\n| ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| **Kinematics** | Differential Drive mobile Robot · Omnidirectional mobile Robot · Omnidirectional with Angular control · Ackermann Steering mobile Robot |\n| **Sensors** | 2D LiDAR · 2D FMCW LiDAR · FOV Detector |\n| **Geometries** | Circle · Rectangle · Polygon · LineString · Binary Grid Map · Fog of Map |\n| **Behaviors** | dash (move directly toward goal) · RVO (Reciprocal Velocity Obstacle) · ORCA (Optimal Reciprocal Collision Avoidance) · SFM (Social Force Model) |\n\n## Documentation\n\n- **English:** [https://ir-sim.readthedocs.io/en](https://ir-sim.readthedocs.io/en)\n- **Chinese (中文):** [https://ir-sim.readthedocs.io/zh-cn](https://ir-sim.readthedocs.io/zh-cn)\n\n## Projects Using IR-SIM\n\n### Academic Publications\n\n- **[RAL & ICRA 2023]** [rl-rvo-nav](https://github.com/hanruihua/rl_rvo_nav) -- Reinforcement learning-based RVO behavior for multi-robot navigation.\n- **[RAL & IROS 2023]** [RDA_planner](https://github.com/hanruihua/RDA_planner) -- Accelerated collision-free motion planner for cluttered environments.\n- **[T-RO 2025]** [NeuPAN](https://github.com/hanruihua/NeuPAN) -- Direct point robot navigation with end-to-end model-based learning.\n\n### Community Projects\n\n- [DRL-robot-navigation-IR-SIM](https://github.com/reiniscimurs/DRL-robot-navigation-IR-SIM) -- Deep reinforcement learning for robot navigation.\n- [AutoNavRL](https://github.com/harshmahesheka/AutoNavRL) -- Autonomous navigation using reinforcement learning.\n- [IRSIM-3DGS-Bridge](https://github.com/Wayneyujie/IRSIM-3DGS-Bridge) -- A closed-loop bridge from 3D Gaussian Splatting scenes to IR-SIM planning/following and back to Habitat-GS trajectory playback.\n\n## Citation\n\nIf you find IR-SIM useful, please consider starring ⭐ this project and citing our paper:\n\n```bibtex\n@article{han2026ir,\n title={IR-SIM: A Lightweight Skill-Native Simulator for Navigation, Learning, and Benchmarking},\n 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},\n journal={arXiv preprint arXiv:2606.08729},\n year={2026},\n doi={10.48550/arXiv.2606.08729},\n url={https://arxiv.org/abs/2606.08729},\n eprint={2606.08729},\n archivePrefix={arXiv},\n primaryClass={cs.RO}\n}\n```\n\n## Contributing\n\nContributions are welcome! 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