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2019

gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo

Lopez, Nestor Gonzalez, Nuin, Yue Leire Erro, Moral, Elias Barba et al.

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This paper presents an upgraded, real world application oriented version of gym-gazebo, the Robot Operating System (ROS) and Gazebo based Reinforcement Learning (RL) toolkit, which complies with OpenAI Gym.

The content discusses the new ROS 2 based software architecture and summarizes the results obtained using Proximal Policy Optimization (PPO).
Ultimately, the output of this work presents a benchmarking system for robotics that allows different techniques and algorithms to be compared using the same virtual conditions.
We have evaluated environments with different levels of complexity of the Modular Articulated Robotic Arm (MARA), reaching accuracies in the millimeter scale.

gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo · Around

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N. Koenig and A. Howard, “Design and use paradigms for gazebo, an open-source multi-robot simulator,” in
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M. Quigley, B. Gerkey, K. Conley, J. Faust, T. Foote, J. Leibs, E. Berger, R. Wheeler, and A. Ng, “Ros: an open-source robot operating system,” in
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Z. C. Lipton, J. Berkowitz, and C. Elkan, “A critical review of recurrent neural networks for sequence learning,”
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2015
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R. Jozefowicz, W. Zaremba, and I. Sutskever, “An empirical exploration of recurrent network architectures,” in
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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,”
Original
2016
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P. Christiano, Z. Shah, I. Mordatch, J. Schneider, T. Blackwell, J. Tobin, P. Abbeel, and W. Zaremba, “Transfer from simulation to real world through learning deep inverse dynamics model,”
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2016
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G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” 2016
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2017

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V. M. Vilches, A. H. Cordero, A. B. Calvo, I. Z. Ugarte, and R. Kojcev, “Robot_gym: accelerated robot training through simulation in the cloud with ros and gazebo,”

2018

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R. Kojcev, N. Etxezarreta, A. Hernández, and V. Mayoral, “Hierarchical learning for modular robots,”

2018

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J. S. Yoon and H. Khalid, “Path planning amidst moving obstacles.”

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T. Zeng, “Learning continuous control through proximal policy optimization for mobile robot navigation.”

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T.-C. Lin, J. Ouyang, D. Qiu, Y. Wang, and Y. Yang, “Teaching ground vehicles to navigate autonomously with reinforcement learning.”

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OpenAI, M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, J. Schneider, S. Sidor, J. Tobin, P. Welinder, L. Weng, and W. Zaremba, “Learning dexterous in-hand manipulation,” 2018

2018

Later among the works it cites.

Y. L. E. Nuin, N. G. Lopez, E. B. Moral, L. U. S. Juan, A. S. Rueda, V. M. Vilches, and R. Kojcev, “Ros2learn: a reinforcement learning framework for ros 2,” 2019

2019

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