Learning Agile Locomotion and Adaptive Behaviors via RL-augmented MPC

G. Feng, H. Zhang, Z. Li, X. B. Peng, B. Basireddy, L. Yue, Z. Song, L. Yang, Y. Liu, K. Sreenath, et al. , “Genloco: Generalized locomotion controllers for quadrupedal robots,” in Conference on Robot Learning . PMLR, 2023, pp. 1893–1903

1903

Earlier work this paper cites.

C. Cao and N. Hovakimyan, “L 1 adaptive controller for a class of systems with unknown nonlinearities: Part i,” in 2008 American Control Conference . IEEE, 2008, pp. 4093–4098

2008

Earlier work this paper cites.

K. Sreenath, H.-W. Park, I. Poulakakis, and J. W. Grizzle, “Embedding active force control within the compliant hybrid zero dynamics to achieve stable, fast running on mabel,” The International Journal of Robotics Research , vol. 32, no. 3, pp. 324–345, 2013

2013

Earlier work this paper cites.

H. Ferreau, C. Kirches, A. Potschka, H. Bock, and M. Diehl, “qpOASES: A parametric active-set algorithm for quadratic programming,” Mathematical Programming Computation , vol. 6, no. 4, pp. 327–363, 2014

2014

Earlier work this paper cites.

Q. Nguyen and K. Sreenath, “L 1 adaptive control for bipedal robots with control lyapunov function based quadratic programs,” in 2015 American Control Conference (ACC) . IEEE, 2015, pp. 862–867

2015

Earlier work this paper cites.

F. Farshidian, M. Neunert, A. W. Winkler, G. Rey, and J. Buchli, “An efficient optimal planning and control framework for quadrupedal locomotion,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 93–100

2017

Earlier work this paper cites.

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017

Original

2017

Earlier work this paper cites.

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, 2018, pp. 1–9

2018

Earlier work this paper cites.

G. Bledt, M. J. Powell, B. Katz, J. Di Carlo, P. M. Wensing, and S. Kim, “Mit cheetah 3: Design and control of a robust, dynamic quadruped robot,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 2245–2252

2018

Earlier work this paper cites.

Q. Nguyen, A. Agrawal, W. Martin, H. Geyer, and K. Sreenath, “Dynamic bipedal locomotion over stochastic discrete terrain,” The International Journal of Robotics Research , vol. 37, no. 13-14, pp. 1537–1553, 2018

2018

Earlier work this paper cites.

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,” arXiv preprint arXiv:1909.06586 , 2019

Original

2019

Earlier work this paper cites.

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, 2020

2020

Earlier work this paper cites.

X. B. Peng, E. Coumans, T. Zhang, T.-W. Lee, J. Tan, and S. Levine, “Learning agile robotic locomotion skills by imitating animals,” arXiv preprint arXiv:2004.00784 , 2020

Original

2020

Earlier work this paper cites.

Y. Ding, A. Pandala, C. Li, Y.-H. Shin, and H.-W. Park, “Representation-free model predictive control for dynamic motions in quadrupeds,” IEEE Transactions on Robotics , vol. 37, no. 4, pp. 1154–1171, 2021

2021

Cited alongside, same era.

N. Rathod, A. Bratta, M. Focchi, M. Zanon, O. Villarreal, C. Semini, and A. Bemporad, “Model predictive control with environment adaptation for legged locomotion,” IEEE Access , vol. 9, pp. 145 710–145 727, 2021

2021

Cited alongside, same era.

R. Grandia, A. J. Taylor, A. D. Ames, and M. Hutter, “Multi-layered safety for legged robots via control barrier functions and model predictive control,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 8352–8358

2021

Cited alongside, same era.

M. V. Minniti, R. Grandia, F. Farshidian, and M. Hutter, “Adaptive clf-mpc with application to quadrupedal robots,” IEEE Robotics and Automation Letters , vol. 7, no. 1, pp. 565–572, 2021

2021

Cited alongside, same era.

J. Sacks and B. Boots, “Learning to optimize in model predictive control,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 10 549–10 556

2022

Later among the works it cites.

Y. Yang, T. Zhang, E. Coumans, J. Tan, and B. Boots, “Fast and efficient locomotion via learned gait transitions,” in Conference on Robot Learning . PMLR, 2022, pp. 773–783

2022

Later among the works it cites.

Z. Xie, X. Da, B. Babich, A. Garg, and M. v. de Panne, “Glide: Generalizable quadrupedal locomotion in diverse environments with a centroidal model,” in International Workshop on the Algorithmic Foundations of Robotics . Springer, 2022, pp. 523–539

2022

Later among the works it cites.

A. Pandala, R. T. Fawcett, U. Rosolia, A. D. Ames, and K. A. Hamed, “Robust predictive control for quadrupedal locomotion: Learning to close the gap between reduced-and full-order models,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 6622–6629, 2022

2022

Later among the works it cites.