SYNLOCO: Synthesizing Central Pattern Generator and Reinforcement Learning for Quadruped Locomotion

D. Kim, J. Di Carlo, B. Katz, G. Bledt, and S. Kim, “Highly dynamic quadruped locomotion via whole-body impulse control and model predictive control.” [Online]. Available: http://arxiv.org/abs/1909.06586

Original

1909

Earlier work this paper cites.

Gen Endo, Jun Nakanishi, Jun Morimoto, and G. Cheng, “Experimental studies of a neural oscillator for biped locomotion with QRIO,” in Proceedings of the 2005 IEEE International Conference on Robotics and Automation . IEEE, pp. 596–602

2005

Earlier work this paper cites.

T. Yoshiike, M. Kuroda, R. Ujino, H. Kaneko, H. Higuchi, S. Iwasaki, Y. Kanemoto, M. Asatani, and T. Koshiishi, “Development of experimental legged robot for inspection and disaster response in plants,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 4869–4876

2017

Cited alongside, same era.

J. Di Carlo, P. M. Wensing, B. Katz, G. Bledt, and S. Kim, “Dynamic locomotion in the MIT cheetah 3 through convex model-predictive control,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 1–9

2018

Cited alongside, same era.

M. Tranzatto, T. Miki, M. Dharmadhikari, L. Bernreiter, M. Kulkarni, F. Mascarich, O. Andersson, S. Khattak, M. Hutter, R. Siegwart, and K. Alexis, “CERBERUS in the DARPA subterranean challenge,” Science Robotics , vol. 7, no. 66, p. eabp9742

Cited in the paper.

P. Arm, G. Waibel, J. Preisig, T. Tuna, R. Zhou, V. Bickel, G. Ligeza, T. Miki, F. Kehl, H. Kolvenbach, and M. Hutter, “Scientific exploration of challenging planetary analog environments with a team of legged robots,” Science Robotics , vol. 8, no. 80, p. eade9548

Cited in the paper.

R. Yuste, J. N. MacLean, J. Smith, and A. Lansner, “The cortex as a central pattern generator,” Nature Reviews Neuroscience , vol. 6, no. 6, pp. 477–483, number: 6 Publisher: Nature Publishing Group

Cited in the paper.

F. Delcomyn, “Neural basis of rhythmic behavior in animals,” Science , vol. 210, no. 4469, pp. 492–498

Cited in the paper.

J. Wang, C. Hu, and Y. Zhu, “CPG-based hierarchical locomotion control for modular quadrupedal robots using deep reinforcement learning,” IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 7193–7200

Cited in the paper.

G. Bellegarda and A. Ijspeert, “CPG-RL: Learning central pattern generators for quadruped locomotion,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 12 547–12 554, conference Name: IEEE Robotics and Automation Letters

Cited in the paper.

M. Thor and P. Manoonpong, “A fast online frequency adaptation mechanism for CPG-based robot motion control,” IEEE Robotics and Automation Letters , vol. 4, no. 4, pp. 3324–3331

Cited in the paper.

M. Thor, T. Kulvicius, and P. Manoonpong, “Generic neural locomotion control framework for legged robots,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 9, pp. 4013–4025, conference Name: IEEE Transactions on Neural Networks and Learning Systems

Cited in the paper.

J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, p. eaau5872

Cited in the paper.

J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science Robotics , vol. 5, no. 47, p. eabc5986

Cited in the paper.

X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel, “Sim-to-real transfer of robotic control with dynamics randomization,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 3803–3810

2018

Later among the works it cites.

D. Kang, F. De Vincenti, N. C. Adami, and S. Coros, “Animal motions on legged robots using nonlinear model predictive control,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 11 955–11 962

2022

Later among the works it cites.