RealAnt: An Open-Source Low-Cost Quadruped for Education and Research in Real-World Reinforcement Learning

A. H. Schoen, Infinite periodic minimal surfaces without self-intersections . National Aeronautics and Space Administration, 1970

1970

Earlier work this paper cites.

G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools , 2000

2000

Earlier work this paper cites.

P. Holoborodko, “Smooth noise robust differentiators,” http://www.holoborodko.com/pavel/numerical-methods/numerical-derivative/smooth-low-noise-differentiators/, 2008

2008

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.

J. Schulman, P. Moritz, S. Levine, M. Jordan, and P. Abbeel, “High-dimensional continuous control using generalized advantage estimation,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2016

2016

Earlier work this paper cites.

S. Garrido-Jurado, R. Munoz-Salinas, F. J. Madrid-Cuevas, and R. Medina-Carnicer, “Generation of fiducial marker dictionaries using mixed integer linear programming,” Pattern Recognition , vol. 51, pp. 481–491, 2016

2016

Earlier work this paper cites.

L. Paull, J. Tani, H. Ahn, J. Alonso-Mora, L. Carlone, M. Cap, Y. F. Chen, C. Choi, J. Dusek, Y. Fang, et al. , “Duckietown: an open, inexpensive and flexible platform for autonomy education and research,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 1497–1504

2017

Earlier work this paper cites.

Y. Tassa, Y. Doron, A. Muldal, T. Erez, Y. Li, D. d. L. Casas, D. Budden, A. Abdolmaleki, J. Merel, A. Lefrancq, et al. , “Deepmind control suite,” arXiv preprint arXiv:1801.00690 , 2018

Original

2018

Earlier work this paper cites.

T. Haarnoja, A. Zhou, K. Hartikainen, G. Tucker, S. Ha, J. Tan, V. Kumar, H. Zhu, A. Gupta, P. Abbeel, et al. , “Soft actor-critic algorithms and applications,” arXiv preprint arXiv:1812.05905 , 2018

Original

2018

Earlier work this paper cites.

A. Gupta, A. Murali, D. P. Gandhi, and L. Pinto, “Robot learning in homes: Improving generalization and reducing dataset bias,” in Advances in Neural Information Processing Systems , 2018, pp. 9094–9104

2018

Cited alongside, same era.

F. J. Romero-Ramirez, R. Muñoz-Salinas, and R. Medina-Carnicer, “Speeded up detection of squared fiducial markers,” Image and vision Computing , vol. 76, pp. 38–47, 2018

2018

Cited alongside, same era.

S. Fujimoto, H. Hoof, and D. Meger, “Addressing function approximation error in actor-critic methods,” in International Conference on Machine Learning , 2018, pp. 1587–1596

2018

Cited alongside, same era.

T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in International Conference on Machine Learning , 2018, pp. 1861–1870

2018

Cited alongside, same era.

D. V. Gealy, S. McKinley, B. Yi, P. Wu, P. R. Downey, G. Balke, A. Zhao, M. Guo, R. Thomasson, A. Sinclair, et al. , “Quasi-direct drive for low-cost compliant robotic manipulation,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 437–443

2019

Later among the works it cites.

B. Yang, J. Zhang, D. Jayaraman, and S. Levine, “REPLAB: A reproducible low-cost arm benchmark platform for robotic learning,” ICRA , 2019

2019

Later among the works it cites.

M. Janner, J. Fu, M. Zhang, and S. Levine, “When to trust your model: model-based policy optimization,” in Advances in Neural Information Processing Systems , 2019, pp. 12 519–12 530

2019

Later among the works it cites.

F. Grimminger, A. Meduri, M. Khadiv, J. Viereck, M. Wüthrich, M. Naveau, V. Berenz, S. Heim, F. Widmaier, T. Flayols, et al. , “An open torque-controlled modular robot architecture for legged locomotion research,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3650–3657, 2020

2020