Fetching the paper…
Reading the bibliography…
Safe deployment of autonomous robots in diverse scenarios requires agents that are capable of efficiently adapting to new environments while satisfying constraints.
1911
Earlier work this paper cites.
Y. Bar-Shalom and E. Tse, “Dual effect, certainty equivalence, and separation in stochastic control,” IEEE Transactions on Automatic Control , vol. 19, no. 5, pp. 494–500, 1974
1974
Earlier work this paper cites.
J.-J. E. Slotine and W. Li, “On the adaptive control of robot manipulators,” Int. Journal of Robotics Research , vol. 6, no. 3, pp. 49–59, 1987
1987
Earlier work this paper cites.
J. Schmidhuber, “Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook,” Ph.D. dissertation, Technische Universität München, 1987
1987
Earlier work this paper cites.
D. J. C. MacKay, “Information-based objective functions for active data selection,” Neural Computation , vol. 4, no. 4, pp. 590–604, 1992
1992
Earlier work this paper cites.
R. Murray, S. S. Sastry, and L. Zexiang, A Mathematical Introduction to Robotic Manipulation . CRC Press, 1994
1994
Earlier work this paper cites.
S. Hochreiter, A. S. Younger, and P. R. Conwell, “Learning to learn using gradient descent,” in International Conference on Artificial Neural Networks , 2001
2001
Earlier work this paper cites.
W. H. Greene, Econometric Analysis , 5th ed. Prentice Hall, 2002
2002
Earlier work this paper cites.
2006
Earlier work this paper cites.
C. Williams and C. E. Rasmussen, Gaussian processes for machine learning . MIT press, 2006
2006
Earlier work this paper cites.
A. Rahimi and B. Ben Recht, “Random features for large-scale kernel machines,” in Conf. on Neural Information Processing Systems , 2007
2007
Earlier work this paper cites.
D. Limon, T. Alamo, D. M. Raimondo, D. Muñoz de la Peña, J. M. Bravo, A. Ferramosca, and E. F. Camacho, Input-to-State Stability: A Unifying Framework for Robust Model Predictive Control . Springer Berlin Heidelberg, 2009, pp. 1–26
2009
Earlier work this paper cites.
L. Blackmore, M. Ono, A. Bektassov, and B. C. Williams, “A probabilistic particle-control approximation of chance-constrained stochastic predictive control,” IEEE Transactions on Robotics , vol. 26, no. 3, pp. 502–517, 2010
2010
Earlier work this paper cites.
N. Srinivas, A. Krause, S. Kakade, and M. Seeger, “Gaussian process optimization in the bandit setting: No regret and experimental design,” in Int. Conf. on Machine Learning , 2010
2010
Earlier work this paper cites.
Y. Abbasi-Yadkori, D. Pál, and C. Szepesvári, “Improved algorithms for linear stochastic bandits,” in Conf. on Neural Information Processing Systems , 2011
2011
Earlier work this paper cites.
L. Blackmore, M. Ono, and B. C. Williams, “Chance-constrained optimal path planning with obstacles,” IEEE Transactions on Robotics , vol. 27, no. 6, pp. 1080–1094, 2011
2011
Earlier work this paper cites.
M. Ono, “Joint chance-constrained model predictive control with probabilistic resolvability,” in American Control Conference , 2012
2012
Earlier work this paper cites.
J. Rawlings and D. Mayne, Model predictive control: Theory and design . Nob Hill Publishing, 2013
2013
Earlier work this paper cites.
A. K. Akametalu, J. F. Fisac, J. H. Gillula, S. Kaynama, M. N. Zeilinger, and C. J. Tomlin, “Reachability-based safe learning with Gaussian processes,” in Proc. IEEE Conf. on Decision and Control , 2014
2014
Earlier work this paper cites.
M. Deisenroth, D. Fox, and C. Rasmussen, “Gaussian processes for data-efficient learning in robotics and control,” IEEE Transactions on Pattern Analysis & Machine Intelligence , vol. 37, no. 2, pp. 408–423, 2015
2015
Earlier work this paper cites.
J. Snoek, O. Rippel, K. Swersky, N. Satish, N. Sundaram, M. M. A. Patwary, P. Prabhat, and R. P. Adams, “Scalable Bayesian optimization using deep neural networks,” in Int. Conf. on Learning Representations , 2015
2015
Earlier work this paper cites.
S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” Journal of Machine Learning Research , vol. 17, pp. 1–40, 2016
2016
Earlier work this paper cites.
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap, “Meta-learning with memory-augmented neural networks,” in Int. Conf. on Machine Learning , 2016
2016
Earlier work this paper cites.
E. D. Klenske and P. Hennig, “Dual control for approximate Bayesian reinforcement learning,” Journal of Machine Learning Research , vol. 17, no. 1, pp. 1–30, 2016
2016
Earlier work this paper cites.
F. Berkenkamp, R. Moriconi, A. P. Schoellig, and A. Krause, “Safe learning of regions of attraction for uncertain, nonlinear systems with Gaussian processes,” in Proc. IEEE Conf. on Decision and Control , 2016
2016
Earlier work this paper cites.
Y. Mao, M. Szmuk, and B. Açikmeşe, “Successive convexification of non-convex optimal control problems and its convergence properties,” in Proc. IEEE Conf. on Decision and Control , 2016
2016
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Int. Conf. on Machine Learning , 2017
2017
Earlier work this paper cites.
F. Berkenkamp, M. Turchetta, A. Schoellig, and A. Krause, “Safe model-based reinforcement learning with stability guarantees,” in Conf. on Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
E. Schmerling and M. Pavone, “Evaluating trajectory collision probability through adaptive importance sampling for safe motion planning,” in Robotics: Science and Systems , 2017
2017
Cited alongside, same era.
A. Chowdhury, S. R. Gopalan, “On kernelized multi-armed bandits,” in Int. Conf. on Machine Learning , 2017
2017
Cited alongside, same era.
S. Bansal, S. L. Chen, M. Herbert, and C. J. Tomlin, “Hamilton-Jacobi reachability: A brief overview and recent advances,” in Proc. IEEE Conf. on Decision and Control , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. Boots, and E. A. Theodorou, “Information theoretic mpc for model-based reinforcement learning,” in Proc. IEEE Conf. on Robotics and Automation , 2017
——, “Data-driven model predictive control with stability and robustness guarantees,” IEEE Transactions on Automatic Control , pp. 1–1, 2020
2020
Closest in time.
S. Kakade, A. Krishnamurthy, K. Lowrey, M. Ohnishi, and W. Sun, “Information theoretic regret bounds for online nonlinear control,” in Conf. on Neural Information Processing Systems , 2020
2020
Closest in time.
L. Hewing, J. Kabzan, and M. N. Zeilinger, “Cautious model predictive control using Gaussian process regression,” IEEE Transactions on Control Systems Technology , vol. 28, no. 6, pp. 2736–2743, 2020
2020
Closest in time.
T. Lew, R. Bonalli, and M. Pavone, “Chance-constrained sequential convex programming for robust trajectory optimization,” in European Control Conference , 2020
2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
R. Amit and R. Meir, “Meta-learning by adjusting priors based on extended PAC-Bayes theory,” in Int. Conf. on Machine Learning , 2018
2018
Cited alongside, same era.
J. Harrison, A. Sharma, and M. Pavone, “Meta-learning priors for efficient online bayesian regression,” in Workshop on Algorithmic Foundations of Robotics , 2018
2018
Cited alongside, same era.
T. Koller, F. Berkenkamp, M. Turchetta, and A. Krause, “Learning-based model predictive control for safe exploration,” in Proc. IEEE Conf. on Decision and Control , 2018
2018
Cited alongside, same era.
F. Berkenkamp, “Safe exploration in reinforcement learning: Theory and applications in robotics,” Ph.D. dissertation, Institute for Machine Learning, ETH Zürich, 2018
2018
Cited alongside, same era.
Y. Chen, H. Peng, J. Grizzle, and N. Ozay, “Data-driven computation of minimal robust control invariant set,” in Proc. IEEE Conf. on Decision and Control , 2018
2018
Cited alongside, same era.
M. Jankovic, “Robust control barrier functions for constrained stabilization of nonlinear systems,” Automatica , vol. 96, pp. 359–367, 2018
2018
Cited alongside, same era.
J. Harrison, A. Sharma, R. Calandra, and M. Pavone, “Control adaptation via meta-learning dynamics,” in NeurIPS Workshop on Meta-Learning , 2018
2018
Cited alongside, same era.
2020
Closest in time.
M. J. Khojasteh, V. Dhiman, M. Franceschetti, and N. Atanasov, “Probabilistic safety constraints for learned high relative degree system dynamics,” in 2nd Annual Conference on Learning for Dynamics & Control , 2020
2020
Closest in time.
R. Cheng, M. J. Khojasteh, A. D. Ames, and J. W. Burdick, “Safe multi-agent interaction through robust control barrier functions with learned uncertainties,” in Proc. IEEE Conf. on Decision and Control , 2020
2020
Closest in time.
K. M. Frey, T. J. Steiner, and J. P. How, “Collision probabilities for continuous-time systems without sampling,” in Robotics: Science and Systems , 2020
2020
Closest in time.
Y. Chow, O. Nachum, A. Faust, E. Duenez-Guzman, and M. Mohammad Ghavamzadeh, “Lyapunov-based safe policy optimization for continuous control,” in Conf. on Robot Learning , 2020
2020
Closest in time.
Y. N. Nakka, A. Liu, G. Shi, A. Anandkumar, Y. Yue, and S. J. Chung, “Chance-constrained trajectory optimization for safe exploration and learning of nonlinear systems,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 389–396, 2020
2020
Closest in time.
P. Geibel and F. Wysotzki, “Risk-sensitive reinforcement learning applied to control under constraints,” Journal of Artificial Intelligence Research , vol. 34, pp. 81–108, 2020
2020
Closest in time.
K. Jia, S. Li, Y. Wen, T. Liu, and D. Tao, “Orthogonal deep neural networks,” IEEE Transactions on Pattern Analysis & Machine Intelligence , 2020
2020
Closest in time.
S. Banerjee, J. Harrison, P. M. Furlong, and M. Pavone, “Adaptive meta-learning for identification of rover-terrain dynamics,” in Int. Symp. on Artificial Intelligence, Robotics and Automation in Space , 2020
2020
Closest in time.
T. Lew and M. Pavone, “Sampling-based reachability analysis: A random set theory approach with adversarial sampling,” in Conf. on Robot Learning , 2020
2020
Closest in time.
D. D. Fan, A. Agha-mohammadi, and E. A. Theodorou, “Deep learning tubes for tube MPC,” in Robotics: Science and Systems , 2020
2020
Closest in time.
J. T. Wilson, V. Borovitskiy, A. Terenin, P. Mostowsky, and M. P. Deisenroth, “Efficiently sampling functions from Gaussian process posteriors,” in Int. Conf. on Machine Learning , 2020
2020
Closest in time.
F. Solowjow and S. Trimpe, “Event-triggered learning,” Automatica , vol. 117, 2020
2020
Closest in time.
J. Harrison, A. Sharma, C. Finn, and M. Pavone, “Continuous meta-learning without tasks,” in Conf. on Neural Information Processing Systems , 2020
2020
Closest in time.
2021
Closest in time.
B. T. Lopez and J.-J. E. Slotine, “Adaptive nonlinear control with contraction metrics,” IEEE Control Systems Letters , vol. 5, no. 1, pp. 205–210, 2021
2021
Closest in time.
S. M. Richards, N. Azizan, J.-J. E. Slotine, and M. Pavone, “Adaptive-control-oriented meta-learning for nonlinear systems,” in Robotics: Science and Systems , 2021
2021
Closest in time.
S. Belkhale, R. Li, G. Kahn, R. McAllister, R. Calandra, and S. Levine, “Model-based meta-reinforcement learning for flight with suspended payloads,” IEEE Robotics and Automation Letters , 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
W. Zhang, M. Tognon, L. Ott, R. Siegwart, and J. Nieto, “Active model learning using informative trajectories for improved closed-loop control on real robots,” in Proc. IEEE Conf. on Robotics and Automation , 2021
2021
Closest in time.
S. Chen, M. Fazlyab, M. Morari, G. J. Pappas, and V. M. Preciado, “Learning region of attraction for nonlinear systems,” in Proc. IEEE Conf. on Decision and Control , 2021
2021
Closest in time.
J. Harrison, “Uncertainty and efficiency in adaptive robot learning and control,” Ph.D. dissertation, Stanford University, 2021
2021
Closest in time.