Projects using Gibson/iGibson

It is exciting to see people using Gibson Environment in embodied AI research. Here is a list of projects using Gibson v1 or iGibson:

K. Chen, J. P. de Vicente, G. Sepulveda, F. Xia, A. Soto, M. Vazquez, and S. Savarese. A behavioral approach to visual navigation with graph localization networks. In RSS, 2019.
Hirose, Noriaki, et al. Deep Visual MPC-Policy Learning for Navigation. arXiv preprint arXiv:1903.02749 (2019). IROS 2019.
Xiangyun Meng, Nathan Ratliff, Yu Xiang and Dieter Fox. Scaling Local Control to Large-Scale Topological Navigation
X. Meng, N. Ratliff, Y. Xiang, and D. Fox, Neural autonomous navigation with riemannian motion policy, in IEEE International Conference on Robotics and Automation (ICRA), 2019.
Kang, Katie, et al. Generalization through simulation: Integrating simulated and real data into deep reinforcement learning for vision-based autonomous flight. arXiv preprint arXiv:1902.03701 (2019). ICRA 2019.
Sax, Alexander, et al. Mid-level visual representations improve generalization and sample efficiency for learning active tasks. arXiv preprint arXiv:1812.11971 (2018).
Shen, William B., et al. Situational Fusion of Visual Representation for Visual Navigation. arXiv preprint arXiv:1908.09073 (2019). ICCV 2019.
Li, Chengshu, et al. HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators. arXiv preprint arXiv:1910.11432 (2019).
Watkins-Valls, David, et al. Learning Your Way Without a Map or Compass: Panoramic Target Driven Visual Navigation. arXiv preprint arXiv:1909.09295 (2019).
Akinola, Iretiayo, et al. Accelerated Robot Learning via Human Brain Signals. arXiv preprint arXiv:1910.00682(2019).
Xia, Fei, et al. Interactive Gibson: A Benchmark for Interactive Navigation in Cluttered Environments. arXiv preprint arXiv:1910.14442 (2019).
Pérez-D’Arpino, et al. Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning. Preprint arXiv:2010.08600, 2020.
Andrey Kurenkov, et alVisuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter. IROS 2020.
Andrey Kurenkov, et al. Multi-Layer Semantic and Geometric Modeling with Neural Message Passing in 3D Scene Graphs for Hierarchical Mechanical Search.
Joanne Truong, et al. Learning Navigation Skills for Legged Robots with Learned Robot Embeddings.

These papers tested policies trained in Gibson v1 on real robots in the physical world:

Xiangyun Meng, Nathan Ratliff, Yu Xiang and Dieter Fox. Scaling Local Control to Large-Scale Topological Navigation
X. Meng, N. Ratliff, Y. Xiang, and D. Fox, Neural autonomous navigation with riemannian motion policy, in IEEE International Conference on Robotics and Automation (ICRA), 2019.
Kang, Katie, et al. Generalization through simulation: Integrating simulated and real data into deep reinforcement learning for vision-based autonomous flight. arXiv preprint arXiv:1902.03701 (2019). ICRA 2019.
Hirose, Noriaki, et al. Deep Visual MPC-Policy Learning for Navigation. arXiv preprint arXiv:1903.02749 (2019). IROS 2019.

If you use Gibson, iGibson or their assets, please consider citing the following papers for iGibson, the Interactive Gibson Environment:

@article{shenigibson,
  title={iGibson, a Simulation Environment for Interactive Tasks in Large Realistic Scenes},
  author={Shen*, Bokui and Xia*, Fei and Li*, Chengshu and Mart{\'i}n-Mart{\'i}n*, Roberto and Fan, Linxi and Wang, Guanzhi and Buch, Shyamal and D’Arpino, Claudia and Srivastava, Sanjana and Tchapmi, Lyne P and  Vainio, Kent and Fei-Fei, Li and Savarese, Silvio},
  journal={arXiv preprint arXiv:2012.02924},
  year={2020}
}

@article{xia2020interactive,
           title={Interactive Gibson Benchmark: A Benchmark for Interactive Navigation in Cluttered Environments},
           author={Xia, Fei and Shen, William B and Li, Chengshu and Kasimbeg, Priya and Tchapmi, Micael Edmond and Toshev, Alexander and Mart{\'\i}n-Mart{\'\i}n, Roberto and Savarese, Silvio},
           journal={IEEE Robotics and Automation Letters},
           volume={5},
           number={2},
           pages={713--720},
           year={2020},
           publisher={IEEE}
         }

and the following paper for Gibson v1:

@inproceedings{xia2018gibson,
  title={Gibson env: Real-world perception for embodied agents},
  author={Xia, Fei and Zamir, Amir R and He, Zhiyang and Sax, Alexander and Malik, Jitendra and Savarese, Silvio},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={9068--9079},
  year={2018}
}