Projects using Gibson¶

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).

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{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}
         }

@techreport{xiagibson2019,
           title = {Gibson Env V2: Embodied Simulation Environments for Interactive Navigation},
           author = {Xia, Fei and Li, Chengshu and Chen, Kevin and Shen, William B and Mart{\'i}n-Mart{\'i}n, Roberto and Hirose, Noriaki and Zamir, Amir R and Fei-Fei, Li and Savarese, Silvio},
           group = {Stanford Vision and Learning Group},
           year = {2019},
           institution = {Stanford University},
           month = {6},
}

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}
}