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The performance of robots in high-level tasks depends on the quality of their lower-level controller, which requires fine-tuning.
W. K. Hastings, “Monte carlo sampling methods using markov chains and their applications,” 1970
1970
Earlier work this paper cites.
K. J. Åström, T. Hägglund, C. C. Hang, and W. K. Ho, “Automatic tuning and adaptation for PID controllers - a survey,” Control Engineering Practice , vol. 1, no. 4, pp. 699–714, 1993
1993
Earlier work this paper cites.
M. Zhuang and D. Atherton, “Automatic tuning of optimum PID controllers,” in IEE Proceedings D (Control Theory and Applications) , vol. 140, no. 3. IET Digital Library, 1993, pp. 216–224
1993
Earlier work this paper cites.
S. Boyd, L. Xiao, and A. Mutapcic, “Subgradient methods,” Lecture notes of EE392o, Stanford University, Autumn Quarter , vol. 2004, pp. 2004–2005, 2003
2003
Earlier work this paper cites.
J. C. Spall, Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control . Hoboken, NJ, USA: Wiley, 2005
2005
Earlier work this paper cites.
C.-C. Yu, Autotuning of PID controllers: A relay feedback approach . Berlin, Germany: Springer, 2006
2006
Earlier work this paper cites.
Y. Li, K. H. Ang, and G. C. Chong, “Patents, software, and hardware for PID control: an overview and analysis of the current art,” IEEE Control Systems Magazine , vol. 26, no. 1, pp. 42–54, 2006
2006
Earlier work this paper cites.
N. J. Killingsworth and M. Krstic, “PID tuning using extremum seeking: online, model-free performance optimization,” IEEE Control Systems Magazine , vol. 26, no. 1, pp. 70–79, 2006
2006
Earlier work this paper cites.
D. A. Bristow, M. Tharayil, and A. G. Alleyne, “A survey of iterative learning control,” IEEE control systems magazine , vol. 26, no. 3, pp. 96–114, 2006
2006
Earlier work this paper cites.
D. J. Lizotte, T. Wang, M. H. Bowling, and D. Schuurmans, “Automatic gait optimization with Gaussian Process Regression,” in Proceedings of the International Joint Conferences on Artificial Intelligence Organization , vol. 7, Hyderabad, India, 2007, pp. 944–949
2007
Earlier work this paper cites.
A. O’dwyer, Handbook of PI and PID controller tuning rules . Singapore: World Scientific, 2009
2009
Earlier work this paper cites.
N. Hovakimyan and C. Cao, ℒ 1 \mathcal{L}_{1} Adaptive Control Theory: Guaranteed Robustness with Fast Adaptation . Philadelphia, PA, USA: SIAM, 2010
2010
Earlier work this paper cites.
R. D. Neidinger, “Introduction to automatic differentiation and matlab object-oriented programming,” SIAM review , vol. 52, no. 3, pp. 545–563, 2010
2010
Earlier work this paper cites.
T. Lee, M. Leok, and N. H. McClamroch, “Geometric tracking control of a quadrotor UAV on SE(3),” in Proceedings of the 49th IEEE Conference on Decision and Control , Atlanta, GA, USA, 2010, pp. 5420–5425
2010
Earlier work this paper cites.
J.-X. Xu, “A survey on iterative learning control for nonlinear systems,” International Journal of Control , vol. 84, no. 7, pp. 1275–1294, 2011
2011
Earlier work this paper cites.
X. Wang and N. Hovakimyan, “ ℒ 1 \mathcal{L}_{1} adaptive controller for nonlinear time-varying reference systems,” Systems & Control Letters , vol. 61, no. 4, pp. 455–463, 2012
2012
Earlier work this paper cites.
S. Trimpe, A. Millane, S. Doessegger, and R. D’Andrea, “A self-tuning LQR approach demonstrated on an inverted pendulum,” IFAC Proceedings Volumes , vol. 47, no. 3, pp. 11 281–11 287, 2014
2014
Earlier work this paper cites.
R. Calandra, N. Gopalan, A. Seyfarth, J. Peters, and M. P. Deisenroth, “Bayesian gait optimization for bipedal locomotion,” in Proceedings of the International Conference on Learning and Intelligent Optimization , Gainesville, FL, USA, 2014, pp. 274–290
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
N. Parikh, S. Boyd et al. , “Proximal algorithms,” Foundations and Trends® in Optimization , vol. 1, no. 3, pp. 127–239, 2014
2014
Earlier work this paper cites.
H. K. Khalil, Nonlinear Control . London, United Kingdom: Pearson, 2015, vol. 406
2015
Earlier work this paper cites.
A. Marco, P. Hennig, J. Bohg, S. Schaal, and S. Trimpe, “Automatic LQR tuning based on Gaussian process global optimization,” in Proceedings of IEEE International Conference on Robotics and Automation , Stockholm, Sweden, 2016, pp. 270–277
2016
Earlier work this paper cites.
F. Berkenkamp, A. P. Schoellig, and A. Krause, “Safe controller optimization for quadrotors with Gaussian processes,” in Proceedings of IEEE International Conference on Robotics and Automation , Stockholm, Sweden, 2016, pp. 491–496
2016
Cited alongside, same era.
2016
Cited alongside, same era.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” 2016
2016
Cited alongside, same era.
P.-B. Wieber, R. Tedrake, and S. Kuindersma, “Modeling and control of legged robots,” in Springer handbook of robotics . Springer, 2016, pp. 1203–1234
2016
2021
Later among the works it cites.
N. A. Vien and G. Neumann, “Differentiable robust LQR layers,” arXiv:2106.05535 , 2021
2021
Later among the works it cites.
W. Edwards, G. Tang, G. Mamakoukas, T. Murphey, and K. Hauser, “Automatic tuning for data-driven model predictive control,” in Proceedings of the IEEE International Conference on Robotics and Automation , Xi’an, China, 2021, pp. 7379–7385
2021
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.
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Cited alongside, same era.
B. Amos and J. Z. Kolter, “OptNet: Differentiable optimization as a layer in neural networks,” in Proceedings of the 34th International Conference on Machine Learning , Sydney, Australia, 2017, pp. 136–145
2017
Cited alongside, same era.
R. R. Duivenvoorden, F. Berkenkamp, N. Carion, A. Krause, and A. P. Schoellig, “Constrained bayesian optimization with particle swarms for safe adaptive controller tuning,” IFAC-PapersOnLine , vol. 50, no. 1, pp. 11 800–11 807, 2017
2017
Cited alongside, same era.
A. S. Polydoros and L. Nalpantidis, “Survey of model-based reinforcement learning: Applications on robotics,” Journal of Intelligent & Robotic Systems , vol. 86, no. 2, pp. 153–173, 2017
2017
Cited alongside, same era.
B. Amos, I. Jimenez, J. Sacks, B. Boots, and J. Z. Kolter, “Differentiable MPC for end-to-end planning and control,” in Proceedings of the 32nd Conference on Neural Information Processing Systems , vol. 31, Montreal, Canada, 2018
2018
Cited alongside, same era.
J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang, “JAX: composable transformations of Python+NumPy programs,” 2018. [Online]. Available: http://github.com/google/jax
2018
Cited alongside, same era.
P. R. Giordano, Q. Delamare, and A. Franchi, “Trajectory generation for minimum closed-loop state sensitivity,” in Proceedings of 2018 IEEE International Conference on Robotics and Automation . IEEE, 2018, pp. 286–293
2018
Cited alongside, same era.
A. G. Baydin, B. A. Pearlmutter, A. A. Radul, and J. M. Siskind, “Automatic differentiation in machine learning: a survey,” Journal of Marchine Learning Research , vol. 18, pp. 1–43, 2018
2018
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: An imperative style, high-performance deep learning library,” in Proceedings of the 33rd Conference on Neural Information Processing Systems , Vancouver, Canada, 2019, pp. 8024–8035
2019
Cited alongside, same era.
S. Müller, A. von Rohr, and S. Trimpe, “Local policy search with Bayesian optimization,” in Advances in Neural Information Processing Systems , 2021
2021
Later among the works it cites.
D. Hanover, P. Foehn, S. Sun, E. Kaufmann, and D. Scaramuzza, “Performance, precision, and payloads: Adaptive nonlinear MPC for quadrotors,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 690–697, 2021
2021
Later among the works it cites.
A. Gahlawat, A. Lakshmanan, L. Song, A. Patterson, Z. Wu, N. Hovakimyan, and E. A. Theodorou, “Contraction ℒ 1 \mathcal{L}_{1} -adaptive control using Gaussian processes,” in Proceedings of the 3rd Conference on Learning for Dynamics and Control , vol. 144, Online, 07 – 08 June 2021, pp. 1027–1040
2021
Later among the works it cites.
M. Parmar, M. Halm, and M. Posa, “Fundamental challenges in deep learning for stiff contact dynamics,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 5181–5188
2021
Later among the works it cites.
A. Loquercio, A. Saviolo, and D. Scaramuzza, “AutoTune: Controller tuning for high-speed flight,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4432–4439, 2022
2022
Closest in time.
W. Jin, T. D. Murphey, D. Kulić, N. Ezer, and S. Mou, “Learning from sparse demonstrations,” IEEE Transactions on Robotics , 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
A. Romero, S. Sun, P. Foehn, and D. Scaramuzza, “Model predictive contouring control for time-optimal quadrotor flight,” IEEE Transactions on Robotics , vol. 38, no. 6, pp. 3340–3356, 2022
2022
Closest in time.
F. Tambon, G. Laberge, L. An, A. Nikanjam, P. S. N. Mindom, Y. Pequignot, F. Khomh, G. Antoniol, E. Merlo, and F. Laviolette, “How to certify machine learning based safety-critical systems? A systematic literature review,” Automated Software Engineering , vol. 29, no. 2, pp. 1–74, 2022
2022
Closest in time.
L. Brunke, M. Greeff, A. W. Hall, Z. Yuan, S. Zhou, J. Panerati, and A. P. Schoellig, “Safe learning in robotics: From learning-based control to safe reinforcement learning,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 5, pp. 411–444, 2022
2022
Closest in time.
Z. Wu, S. Cheng, K. A. Ackerman, A. Gahlawat, A. Lakshmanan, P. Zhao, and N. Hovakimyan, “ ℒ 1 \mathcal{L}_{1} adaptive augmentation for geometric tracking control of quadrotors,” in Proceedings of the International Conference on Robotics and Automation , Philadelphia, PA, USA, 2022, pp. 1329–1336
2022
Closest in time.
2022
Closest in time.
S. Cheng, L. Song, M. Kim, S. Wang, and N. Hovakimyan, “Difftune + : Hyperparameter-free auto-tuning using auto-differentiation,” in Learning for Dynamics and Control Conference . PMLR, 2023, pp. 170–183
2023
Closest in time.
2023
Closest in time.
A. Srour, A. Franchi, and P. R. Giordano, “Controller and trajectory optimization for a quadrotor uav with parametric uncertainty,” in Proceedings of 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2023, pp. 1–7
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.