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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A Simulation Environment to train Robots in Large Realistic Interactive Scenes
| Date | Stars |
|---|---|
| 2026-07-25 | 808 |
| 2026-07-28 | 808 |
| 2026-07-30 | 808 |
| 2026-08-06 | 808 |
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# iGibson: A Simulation Environment to train Robots in Large Realistic Interactive Scenes
<img src="./docs/images/igibsonlogo.png" width="500"> <img src="./docs/images/igibson.gif" width="250">
iGibson is a simulation environment providing fast visual rendering and physics simulation based on Bullet. iGibson is equipped with fifteen fully interactive high quality scenes, hundreds of large 3D scenes reconstructed from real homes and offices, and compatibility with datasets like CubiCasa5K and 3D-Front, providing 8000+ additional interactive scenes. Some of the features of iGibson include domain randomization, integration with motion planners and easy-to-use tools to collect human demonstrations. With these scenes and features, iGibson allows researchers to train and evaluate robotic agents that use visual signals to solve navigation and manipulation tasks such as opening doors, picking up and placing objects, or searching in cabinets.
### Latest Updates
[8/9/2021] Major update to iGibson to reach iGibson 2.0, for details please refer to our [arxiv preprint](https://arxiv.org/abs/2108.03272).
- iGibson 2.0 supports object states, including temperature, wetness level, cleanliness level, and toggled and sliced states, necessary to cover a wider range of tasks.
- iGibson 2.0 implements a set of predicate logic functions that map the simulator states to logic states like Cooked or Soaked.
- iGibson 2.0 includes a virtual reality (VR) interface to immerse humans in its scenes to collect demonstrations.
[12/1/2020] Major update to iGibson to reach iGibson 1.0, for details please refer to our [arxiv preprint](https://arxiv.org/abs/2012.02924).
- Release of iGibson dataset that includes 15 fully interactive scenes and 500+ object models annotated with materials and physical attributes on top of [existing 3D articulated models](https://cs.stanford.edu/~kaichun/partnet/).
- Compatibility to import [CubiCasa5K](https://github.com/CubiCasa/CubiCasa5k) and [3D-Front](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset) scene descriptions leading to more than 8000 extra interactive scenes!
- New features in iGibson: Physically based rendering, 1-beam and 16-beam LiDAR, domain randomization, motion planning integration, tools to collect human demos and more!
- Code refactoring, better class structure and cleanup.
[05/14/2020] Added dynamic light support :flashlight:
[04/28/2020] Added support for Mac OSX :computer:
### Citation
If you use iGibson or its assets and models, consider citing the following publication:
```
@inproceedings{li2022igibson,
title = {iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks},
author = {Li, Chengshu and Xia, Fei and Mart\'in-Mart\'in, Roberto and Lingelbach, Michael and Srivastava, Sanjana and Shen, Bokui and Vainio, Kent Elliott and Gokmen, Cem and Dharan, Gokul and Jain, Tanish and Kurenkov, Andrey and Liu, Karen and Gweon, Hyowon and Wu, Jiajun and Fei-Fei, Li and Savarese, Silvio},
booktitle = {Proceedings of the 5th Conference on Robot Learning},
pages = {455--465},
year = {2022},
editor = {Faust, Aleksandra and Hsu, David and Neumann, Gerhard},
volume = {164},
series = {Proceedings of Machine Learning Research},
month = {08--11 Nov},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v164/li22b/li22b.pdf},
url = {https://proceedings.mlr.press/v164/li22b.html},
}
```
```
@inproceedings{shen2021igibson,
title={iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes},
author={Bokui Shen and Fei Xia and Chengshu Li and Roberto Mart\'in-Mart\'in and Linxi Fan and Guanzhi Wang and Claudia P\'erez-D'Arpino and Shyamal Buch and Sanjana Srivastava and Lyne P. Tchapmi and Micael E. Tchapmi and Kent Vainio and Josiah Wong and Li Fei-Fei and Silvio Savarese},
booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and SystExcerpt of 7,004 characters
Read on GitHub
Fei Xia · Google, Inc. · United States
1.4k
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Jerry Zhi-Yang He · UC Berkeley
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Amir Zamir · EPFL. Stanford. UC Berkeley. UCF.
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Andrey Kurenkov · Stanford · United States
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Kevin Chen · Stanford University
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8a13732e28ef9619, topic:simulation, readme:robot learning, readme:manipulation