Built on

A. Murali, T. Chen, K. V. Alwala, D. Gandhi, L. Pinto, S. Gupta, and A. Gupta, “Pyrobot: An open-source robotics framework for research and benchmarking,” CoRR , vol. abs/1906.08236, 2019. [Online]. Available: http://arxiv.org/abs/1906.08236
Original
1906
Earlier work this paper cites.
D. S. Chaplot, D. Gandhi, S. Gupta, A. Gupta, and R. Salakhutdinov, “Learning to explore using active neural SLAM,” CoRR , vol. abs/2004.05155, 2020. [Online]. Available: https://arxiv.org/abs/2004.05155
Original
2004
Earlier work this paper cites.
N. Koenig and A. Howard, “Design and use paradigms for gazebo, an open-source multi-robot simulator,” in 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) , vol. 3, 2004, pp. 2149–2154 vol.3
2004
Earlier work this paper cites.
J. Kramer and M. Scheutz, “Development environments for autonomous mobile robots: A survey,” Auton. Robots , vol. 22, pp. 101–132, 02 2007
2007
Earlier work this paper cites.
M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Ng, “Ros: an open-source robot operating system,” vol. 3, 01 2009
2009
Earlier work this paper cites.
A. Elkady and T. Sobh, “Robotics middleware: A comprehensive literature survey and attribute-based bibliography,” Journal of Robotics , vol. 2012, 05 2012
2012
Earlier work this paper cites.
S. Kohlbrecher, J. Meyer, T. Graber, K. Petersen, U. Klingauf, and O. von Stryk, “Hector open source modules for autonomous mapping and navigation with rescue robots,” in RoboCup 2013: Robot World Cup XVII , S. Behnke, M. Veloso, A. Visser, and R. Xiong, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014, pp. 624–631
2014
Earlier work this paper cites.
G. Magyar, P. Sincak, and Z. Krizsán, “Comparison study of robotic middleware for robotic applications,” Advances in Intelligent Systems and Computing , vol. 316, pp. 121–128, 01 2015
2015
Earlier work this paper cites.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” arXiv preprint arXiv:1606.01540 , 2016
Original
2016
Earlier work this paper cites.
A. Gaidon, Q. Wang, Y. Cabon, and E. Vig, “Virtual worlds as proxy for multi-object tracking analysis,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4340–4349
2016
Earlier work this paper cites.
W. Masson, P. Ranchod, and G. Konidaris, “Reinforcement learning with parameterized actions,” in Thirtieth AAAI Conference on Artificial Intelligence , 2016
2016
Earlier work this paper cites.
A. Hentout, A. Maoudj, and B. Bouzouia, “A survey of development frameworks for robotics,” in 2016 8th International Conference on Modelling, Identification and Control (ICMIC) , 2016, pp. 67–72
2016
Earlier work this paper cites.
A. Hentout, A. Maoudj, and B. Bouzouia, “A survey of development frameworks for robotics,” in 2016 8th International Conference on Modelling, Identification and Control (ICMIC) , 2016, pp. 67–72
2016
Earlier work this paper cites.
I. Zamora, N. G. Lopez, V. M. Vilches, and A. H. Cordero, “Extending the openai gym for robotics: a toolkit for reinforcement learning using ROS and gazebo,” CoRR , vol. abs/1608.05742, 2016. [Online]. Available: http://arxiv.org/abs/1608.05742
Original
2016
Earlier work this paper cites.

Similar

“Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras,” IEEE Transactions on Robotics , vol. 33, no. 5, pp. 1255–1262, 2017
2017
Cited alongside, same era.
L. Tai, G. Paolo, and M. Liu, “Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 31–36
2017
Cited alongside, same era.
A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, and Y. Zhang, “Matterport3d: Learning from rgb-d data in indoor environments,” arXiv preprint arXiv:1709.06158 , 2017
Original
2017
Cited alongside, same era.
A. Juliani, V. Berges, E. Vckay, Y. Gao, H. Henry, M. Mattar, and D. Lange, “Unity: A general platform for intelligent agents,” CoRR , vol. abs/1809.02627, 2018. [Online]. Available: http://arxiv.org/abs/1809.02627
Original
2018
Cited alongside, same era.

Then

H. Oliff, Y. Liu, M. Kumar, M. Williams, and M. Ryan, “Reinforcement learning for facilitating human-robot-interaction in manufacturing,” Journal of Manufacturing Systems , vol. 56, pp. 326–340, 2020
2020
Later among the works it cites.
S. Datta, O. Maksymets, J. Hoffman, S. Lee, D. Batra, and D. Parikh, “Integrating egocentric localization for more realistic point-goal navigation agents,” arXiv preprint arXiv:2009.03231 , 2020
Original
2020
Later among the works it cites.
P. Nikdel, M. Chen, and R. Vaughan, “Recognizing and tracking high-level, human-meaningful navigation features of occupancy grid maps,” in 2020 17th Conference on Computer and Robot Vision (CRV) . IEEE, 2020, pp. 62–69
2020
Later among the works it cites.
E. Marchesini and A. Farinelli, “Discrete deep reinforcement learning for mapless navigation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 10 688–10 694
2020
Later among the works it cites.

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2021

ROS-X-Habitat: Bridging the ROS Ecosystem with Embodied AI

Chen, Guanxiong, Yang, Haoyu, Mitchell, Ian M.

Understand

We introduce ROS-X-Habitat, a software interface that bridges the AI Habitat platform for embodied learning-based agents with other robotics resources via ROS.

This interface not only offers standardized communication protocols between embodied agents and simulators, but also enables physically and photorealistic simulation that benefits the training and/or testing of vision-based embodied agents.
With this interface, roboticists can evaluate their own Habitat RL agents in another ROS-based simulator or use Habitat Sim v2 as the test bed for their own robotic algorithms.
Through in silico experiments, we demonstrate that ROS-X-Habitat has minimal impact on the navigation performance and simulation speed of a Habitat RGBD agent; that a standard set of ROS mapping, planning and navigation tools can run in Habitat Sim v2; and that a Habitat agent can run in the standard ROS simulator Gazebo.

Built on

A. Murali, T. Chen, K. V. Alwala, D. Gandhi, L. Pinto, S. Gupta, and A. Gupta, “Pyrobot: An open-source robotics framework for research and benchmarking,” CoRR , vol. abs/1906.08236, 2019. [Online]. Available: http://arxiv.org/abs/1906.08236
Original
1906
Earlier work this paper cites.
D. S. Chaplot, D. Gandhi, S. Gupta, A. Gupta, and R. Salakhutdinov, “Learning to explore using active neural SLAM,” CoRR , vol. abs/2004.05155, 2020. [Online]. Available: https://arxiv.org/abs/2004.05155
Original
2004
Earlier work this paper cites.
N. Koenig and A. Howard, “Design and use paradigms for gazebo, an open-source multi-robot simulator,” in 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) , vol. 3, 2004, pp. 2149–2154 vol.3
2004
Earlier work this paper cites.
J. Kramer and M. Scheutz, “Development environments for autonomous mobile robots: A survey,” Auton. Robots , vol. 22, pp. 101–132, 02 2007
2007
Earlier work this paper cites.
M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Ng, “Ros: an open-source robot operating system,” vol. 3, 01 2009
2009
Earlier work this paper cites.
A. Elkady and T. Sobh, “Robotics middleware: A comprehensive literature survey and attribute-based bibliography,” Journal of Robotics , vol. 2012, 05 2012
2012
Earlier work this paper cites.
S. Kohlbrecher, J. Meyer, T. Graber, K. Petersen, U. Klingauf, and O. von Stryk, “Hector open source modules for autonomous mapping and navigation with rescue robots,” in RoboCup 2013: Robot World Cup XVII , S. Behnke, M. Veloso, A. Visser, and R. Xiong, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014, pp. 624–631
2014
Earlier work this paper cites.
G. Magyar, P. Sincak, and Z. Krizsán, “Comparison study of robotic middleware for robotic applications,” Advances in Intelligent Systems and Computing , vol. 316, pp. 121–128, 01 2015
2015
Earlier work this paper cites.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” arXiv preprint arXiv:1606.01540 , 2016
Original
2016
Earlier work this paper cites.
A. Gaidon, Q. Wang, Y. Cabon, and E. Vig, “Virtual worlds as proxy for multi-object tracking analysis,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4340–4349
2016
Earlier work this paper cites.
W. Masson, P. Ranchod, and G. Konidaris, “Reinforcement learning with parameterized actions,” in Thirtieth AAAI Conference on Artificial Intelligence , 2016
2016
Earlier work this paper cites.
A. Hentout, A. Maoudj, and B. Bouzouia, “A survey of development frameworks for robotics,” in 2016 8th International Conference on Modelling, Identification and Control (ICMIC) , 2016, pp. 67–72
2016
Earlier work this paper cites.
A. Hentout, A. Maoudj, and B. Bouzouia, “A survey of development frameworks for robotics,” in 2016 8th International Conference on Modelling, Identification and Control (ICMIC) , 2016, pp. 67–72
2016
Earlier work this paper cites.
I. Zamora, N. G. Lopez, V. M. Vilches, and A. H. Cordero, “Extending the openai gym for robotics: a toolkit for reinforcement learning using ROS and gazebo,” CoRR , vol. abs/1608.05742, 2016. [Online]. Available: http://arxiv.org/abs/1608.05742
Original
2016
Earlier work this paper cites.

Similar

“Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras,” IEEE Transactions on Robotics , vol. 33, no. 5, pp. 1255–1262, 2017
2017
Cited alongside, same era.
L. Tai, G. Paolo, and M. Liu, “Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 31–36
2017
Cited alongside, same era.
A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, and Y. Zhang, “Matterport3d: Learning from rgb-d data in indoor environments,” arXiv preprint arXiv:1709.06158 , 2017
Original
2017
Cited alongside, same era.
A. Juliani, V. Berges, E. Vckay, Y. Gao, H. Henry, M. Mattar, and D. Lange, “Unity: A general platform for intelligent agents,” CoRR , vol. abs/1809.02627, 2018. [Online]. Available: http://arxiv.org/abs/1809.02627
Original
2018
Cited alongside, same era.

Then

H. Oliff, Y. Liu, M. Kumar, M. Williams, and M. Ryan, “Reinforcement learning for facilitating human-robot-interaction in manufacturing,” Journal of Manufacturing Systems , vol. 56, pp. 326–340, 2020
2020
Later among the works it cites.
S. Datta, O. Maksymets, J. Hoffman, S. Lee, D. Batra, and D. Parikh, “Integrating egocentric localization for more realistic point-goal navigation agents,” arXiv preprint arXiv:2009.03231 , 2020
Original
2020
Later among the works it cites.
P. Nikdel, M. Chen, and R. Vaughan, “Recognizing and tracking high-level, human-meaningful navigation features of occupancy grid maps,” in 2020 17th Conference on Computer and Robot Vision (CRV) . IEEE, 2020, pp. 62–69
2020
Later among the works it cites.
E. Marchesini and A. Farinelli, “Discrete deep reinforcement learning for mapless navigation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 10 688–10 694
2020
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

A. Garcia-Garcia, P. Martinez-Gonzalez, S. Oprea, J. A. Castro-Vargas, S. Orts-Escolano, J. Garcia-Rodriguez, and A. Jover-Alvarez, “The robotrix: An extremely photorealistic and very-large-scale indoor dataset of sequences with robot trajectories and interactions,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 6790–6797

2018

Cited alongside, same era.

G. Brunner, O. Richter, Y. Wang, and R. Wattenhofer, “Teaching a machine to read maps with deep reinforcement learning,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 32, no. 1, Apr. 2018. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/11645

2018

Cited alongside, same era.

F. Xia, A. R. Zamir, Z. He, A. Sax, J. Malik, and S. Savarese, “Gibson env: Real-world perception for embodied agents,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 9068–9079

2018

Cited alongside, same era.

K. D. Julian and M. J. Kochenderfer, “Distributed wildfire surveillance with autonomous aircraft using deep reinforcement learning,” Journal of Guidance, Control, and Dynamics , vol. 42, no. 8, pp. 1768–1778, 2019

2019

Cited alongside, same era.

F. Monteiro, A. L. Vieira e Silva, J. M. Teixeira, and V. Teichrieb, “Simulating real robots in virtual environments using nvidia’s isaac sdk,” 10 2019, pp. 47–48

2019

Cited alongside, same era.

M. Savva, A. Kadian, O. Maksymets, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik et al. , “Habitat: A platform for embodied ai research,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 9339–9347

2019

Cited alongside, same era.

Z. Liang, Z. Cai, M. Li, and W. Yang, “Parallel gym gazebo: a scalable parallel robot deep reinforcement learning platform,” in 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) , 2019, pp. 206–213

2019

Cited alongside, same era.

M. Labbé and F. Michaud, “Rtab-map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation,” Journal of Field Robotics , vol. 36, no. 2, pp. 416–446, 2019. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.21831

2019

Cited alongside, same era.

F. Xiang, Y. Qin, K. Mo, Y. Xia, H. Zhu, F. Liu, M. Liu, H. Jiang, Y. Yuan, H. Wang et al. , “Sapien: A simulated part-based interactive environment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 097–11 107

2020

Later among the works it cites.

A. Kadian, J. Truong, A. Gokaslan, A. Clegg, E. Wijmans, S. Lee, M. Savva, S. Chernova, and D. Batra, “Sim2real predictivity: Does evaluation in simulation predict real-world performance,” IEEE Robotics and Automation Letters , vol. PP, pp. 1–1, 08 2020

2020

Later among the works it cites.

“Bullet physics sdk,” https://github.com/bulletphysics/bullet3 , accessed: 2021-09-03

2021

Closest in time.

G. Chen, H. Yang, and I. M. Mitchell, “Ros-x-habitat: Bridging the ros ecosystem with embodied ai,” arXiv preprint arXiv:2109.07703 , 2021

Original

2021

Closest in time.

A. Szot, A. Clegg, E. Undersander, E. Wijmans, Y. Zhao, J. Turner, N. Maestre, M. Mukadam, D. Chaplot, O. Maksymets et al. , “Habitat 2.0: Training home assistants to rearrange their habitat,” arXiv preprint arXiv:2106.14405 , 2021

Original

2021

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A.-I. Toma, H.-Y. Hsueh, H. A. Jaafar, R. Murai, P. H. Kelly, and S. Saeedi, “Pathbench: A benchmarking platform for classical and learned path planning algorithms,” in 2021 18th Conference on Robots and Vision (CRV) . IEEE, 2021, pp. 79–86

2021

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J. Duan, S. Yu, H. L. Tan, H. Zhu, and C. Tan, “A survey of embodied ai: From simulators to research tasks,” IEEE Transactions on Emerging Topics in Computational Intelligence , pp. 1–15, 2022

2022

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“Locobot an open source low cost robot,” http://www.locobot.org/ , accessed: 2022-02-24

2022

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H. Yang, “Ros-x-habitat: Bridging the ros ecosystem with embodied ai,” 2022. [Online]. Available: http://hdl.handle.net/2429/81124

2022

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