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In this paper, we propose a multi-domain control parameter learning framework that combines Bayesian Optimization (BO) and Hybrid Zero Dynamics (HZD) for locomotion control of bipedal robots.
D. R. Jones, “A taxonomy of global optimization methods based on response surfaces,” Journal of global optimization , vol. 21, no. 4, pp. 345–383, 2001
2001
Earlier work this paper cites.
C. E. Rasmussen, “Gaussian processes in machine learning,” in Summer school on machine learning . Springer, 2003, pp. 63–71
2003
Earlier work this paper cites.
D. J. Lizotte, T. Wang, M. H. Bowling, and D. Schuurmans, “Automatic gait optimization with gaussian process regression.” in IJCAI , vol. 7, 2007, pp. 944–949
2007
Earlier work this paper cites.
J. W. Grizzle, C. Chevallereau, A. Ames, and R. Sinnet, “3d bipedal robotic walking: Models, feedback control, and open problems,” in IFAC Symposium on Nonlinear Control Systems , 2010
2010
Earlier work this paper cites.
N. Srinivas, A. Krause, S. M. Kakade, and M. W. Seeger, “Gaussian process optimization in the bandit setting: No regret and experimental design,” in Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel , J. Fürnkranz and T. Joachims, Eds. Omnipress, 2010, pp. 1015–1022. [Online]. Available: https://icml.cc/Conferences/2010/papers/422.pdf
2010
Earlier work this paper cites.
A. D. Bull, “Convergence rates of efficient global optimization algorithms.” Journal of Machine Learning Research , vol. 12, no. 10, 2011
2011
Earlier work this paper cites.
M. Tesch, J. Schneider, and H. Choset, “Using response surfaces and expected improvement to optimize snake robot gait parameters,” in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2011, pp. 1069–1074
2011
Earlier work this paper cites.
J. Mockus, Bayesian approach to global optimization: theory and applications . Springer Science & Business Media, 2012, vol. 37
2012
Earlier work this paper cites.
M. A. Gelbart, J. Snoek, and R. P. Adams, “Bayesian optimization with unknown constraints,” in Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence , ser. UAI’14. Arlington, Virginia, USA: AUAI Press, 2014, p. 250–259
2014
Earlier work this paper cites.
Y. Sui, A. Gotovos, J. Burdick, and A. Krause, “Safe exploration for optimization with gaussian processes,” in International Conference on Machine Learning . PMLR, 2015, pp. 997–1005
2015
Earlier work this paper cites.
J. Schreiter, D. Nguyen-Tuong, M. Eberts, B. Bischoff, H. Markert, and M. Toussaint, “Safe exploration for active learning with gaussian processes,” in Joint European conference on machine learning and knowledge discovery in databases . Springer, 2015, pp. 133–149
2015
Earlier work this paper cites.
X. Da, O. Harib, R. Hartley, B. Griffin, and J. W. Grizzle, “From 2d design of underactuated bipedal gaits to 3d implementation: Walking with speed tracking,” IEEE Access , vol. 4, pp. 3469–3478, 2016
2016
Cited alongside, same era.
Q. Nguyen, X. Da, J. W. Grizzle, and K. Sreenath, “Dynamic walking on stepping stones with gait library and control barrier,” in Workshop on Algorithimic Foundations of Robotics (WAFR) , 2016
2016
Cited alongside, same era.
R. Calandra, A. Seyfarth, J. Peters, and M. P. Deisenroth, “Bayesian optimization for learning gaits under uncertainty,” Annals of Mathematics and Artificial Intelligence , vol. 76, no. 1, pp. 5–23, 2016
2016
Cited alongside, same era.
F. Berkenkamp, A. P. Schoellig, and A. Krause, “Safe controller optimization for quadrotors with gaussian processes,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) , 2016, pp. 491–496
2016
Cited alongside, same era.
M. Tucker, E. Novoseller, C. Kann, Y. Sui, Y. Yue, J. W. Burdick, and A. D. Ames, “Preference-based learning for exoskeleton gait optimization,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 2351–2357
2020
Later among the works it cites.
F. Berkenkamp, A. Krause, and A. P. Schoellig, “Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics,” Machine Learning , pp. 1–35, 2021
2021
Later among the works it cites.
Z. Li, X. Cheng, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath, “Reinforcement learning for robust parameterized locomotion control of bipedal robots,” in International Conference on Robotics and Automation (ICRA) , 2021, pp. 2811–2817
2021
Later among the works it cites.
J. Siekmann, Y. Godse, A. Fern, and J. Hurst, “Sim-to-real learning of all common bipedal gaits via periodic reward composition,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 7309–7315
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A. Hereid and A. D. Ames, “Frost: Fast robot optimization and simulation toolkit,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 719–726
2017
Cited alongside, same era.
Q. Nguyen, X. Da, W. Martin, H. Geyer, J. W. Grizzle, and Sreenath, “Dynamic walking on randomly-varying discrete terrain with one-step preview,” in Robotics: Science and Systems (RSS) , 2017
2017
Cited alongside, same era.
P. I. Frazier, “A tutorial on bayesian optimization,” stat , vol. 1050, p. 8, 2018
2018
Cited alongside, same era.
A. Hereid, O. Harib, R. Hartley, Y. Gong, and J. W. Grizzle, “Rapid trajectory optimization using c-frost with illustration on a cassie-series dynamic walking biped,” pp. 4722–4729, 2019
2019
Cited alongside, same era.
Y. Gong, R. Hartley, X. Da, A. Hereid, O. Harib, J.-K. Huang, and J. Grizzle, “Feedback control of a cassie bipedal robot: Walking, standing, and riding a segway,” in 2019 American Control Conference (ACC) , 2019, pp. 4559–4566
2019
Cited alongside, same era.
Z. Li, C. Cummings, and K. Sreenath, “Animated cassie: A dynamic relatable robotic character,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 3739–3746
2020
Cited alongside, same era.
Z. Xie, P. Clary, J. Dao, P. Morais, J. Hurst, and M. Panne, “Learning locomotion skills for cassie: Iterative design and sim-to-real,” in Conference on Robot Learning . PMLR, 2020, pp. 317–329
2020
Cited alongside, same era.
2021
Later among the works it cites.
G. A. Castillo, B. Weng, W. Zhang, and A. Hereid, “Robust feedback motion policy design using reinforcement learning on a 3d digit bipedal robot,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 5136–5143
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
G. Ryou, E. Tal, and S. Karaman, “Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers,” The International Journal of Robotics Research , p. 02783649211033317, 2021
2021
Later among the works it cites.
A. Marco, D. Baumann, M. Khadiv, P. Hennig, L. Righetti, and S. Trimpe, “Robot learning with crash constraints,” IEEE Robotics and Automation Letters , 2021
2021
Later among the works it cites.