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This article proposes a hierarchical learning architecture for safe data-driven control in unknown environments.
M. Spong and M. Vidyasagar, Robot Dynamics And Control , 01 1989
1989
Earlier work this paper cites.
G. Papadopoulos, P. J. Edwards, and A. F. Murray, “Confidence estimation methods for neural networks: A practical comparison,” IEEE transactions on neural networks , vol. 12, no. 6, pp. 1278–1287, 2001
2001
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.
A. R. Nandeshwar, “Models for calculating confidence intervals for neural networks,” 2006
2006
Earlier work this paper cites.
Y. Tassa, T. Erez, and W. D. Smart, “Receding horizon differential dynamic programming,” in Advances in neural information processing systems , 2008, pp. 1465–1472
2008
Earlier work this paper cites.
L. Torrey and J. Shavlik, “Transfer learning,” Handbook of Research on Machine Learning Applications , 01 2009
2009
Earlier work this paper cites.
C. Liu and C. G. Atkeson, “Standing balance control using a trajectory library,” in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems . Citeseer, 2009, pp. 3031–3036
2009
Earlier work this paper cites.
D. J. Hoelzle, A. G. Alleyne, and A. J. W. Johnson, “Basis task approach to iterative learning control with applications to micro-robotic deposition,” IEEE Transactions on Control Systems Technology , vol. 19, no. 5, pp. 1138–1148, 2010
2010
Earlier work this paper cites.
M. Stolle and C. Atkeson, “Finding and transferring policies using stored behaviors,” Autonomous Robots , vol. 29, no. 2, pp. 169–200, 2010
2010
Earlier work this paper cites.
M. Stolle and C. Atkeson, “Finding and transferring policies using stored behaviors,” Autonomous Robots , vol. 29, no. 2, pp. 169–200, 2010
2010
Earlier work this paper cites.
A. Khosravi, S. Nahavandi, D. Creighton, and A. F. Atiya, “Lower upper bound estimation method for construction of neural network-based prediction intervals,” IEEE Transactions on Neural Networks , vol. 22, no. 3, pp. 337–346, 2011
2011
Earlier work this paper cites.
R. Rajamani, Vehicle dynamics and control . Springer Science & Business Media, 2011
2011
Earlier work this paper cites.
D. Berenson, P. Abbeel, and K. Goldberg, “A robot path planning framework that learns from experience,” in 2012 IEEE International Conference on Robotics and Automation . IEEE, 2012, pp. 3671–3678
2012
Earlier work this paper cites.
A. Paraschos, G. Neumann, and J. Peters, “A probabilistic approach to robot trajectory generation,” in 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids) . IEEE, 2013, pp. 477–483
2013
Earlier work this paper cites.
E. D. Klenske, M. N. Zeilinger, B. Schölkopf, and P. Hennig, “Gaussian process-based predictive control for periodic error correction,” IEEE Transactions on Control Systems Technology , vol. 24, no. 1, pp. 110–121, 2015
2015
Earlier work this paper cites.
J. van Zundert, J. Bolder, and T. Oomen, “Optimality and flexibility in iterative learning control for varying tasks,” Automatica , vol. 67, pp. 295–302, 2016
2016
Earlier work this paper cites.
K. Weiss, T. M. Khoshgoftaar, and D. Wang, “A survey of transfer learning,” Journal of Big data , vol. 3, no. 1, p. 9, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
U. Rosolia, A. Carvalho, and F. Borrelli, “Autonomous racing using learning model predictive control,” in 2017 American Control Conference (ACC) . IEEE, 2017, pp. 5115–5120
2017
Cited alongside, same era.
T. Oomen and C. R. Rojas, “Sparse iterative learning control with application to a wafer stage: Achieving performance, resource efficiency, and task flexibility,” Mechatronics , vol. 47, pp. 134–147, 2017
2017
Cited alongside, same era.
U. Rosolia and F. Borrelli, “Learning model predictive control for iterative tasks. a data-driven control framework,” IEEE Transactions on Automatic Control , vol. 63, no. 7, pp. 1883–1896, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. Hofer, L. Spannagl, and R. D’Andrea, “Iterative learning control for fast and accurate position tracking with an articulated soft robotic arm,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 6602–6607
2019
Later among the works it cites.
M. Ketelhut, S. Stemmler, J. Gesenhues, M. Hein, and D. Abel, “Iterative learning control of ventricular assist devices with variable cycle durations,” Control Engineering Practice , vol. 83, pp. 33–44, 2019
2019
Later among the works it cites.
J. Kabzan, L. Hewing, A. Liniger, and M. N. Zeilinger, “Learning-based model predictive control for autonomous racing,” IEEE Robotics and Automation Letters , vol. 4, no. 4, pp. 3363–3370, 2019
2019
Later among the works it cites.
M. K. Cobb, K. Barton, H. Fathy, and C. Vermillion, “Iterative learning-based path optimization for repetitive path planning, with application to 3-d crosswind flight of airborne wind energy systems,” IEEE Transactions on Control Systems Technology , pp. 1–13, 2019
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2018
Cited alongside, same era.
O. Nachum, S. S. Gu, H. Lee, and S. Levine, “Data-efficient hierarchical reinforcement learning,” in Advances in neural information processing systems , 2018, pp. 3303–3313
2018
Cited alongside, same era.
D. Bertsimas and B. Stellato, “The voice of optimization,” 2018
2018
Cited alongside, same era.
K. Pereida, M. K. Helwa, and A. P. Schoellig, “Data-efficient multirobot, multitask transfer learning for trajectory tracking,” IEEE Robotics and Automation Letters , vol. 3, no. 2, pp. 1260–1267, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
A. Jain, T. X. Nghiem, M. Morari, and R. Mangharam, “Learning and control using gaussian processes: Towards bridging machine learning and controls for physical systems,” in Proceedings of the 9th ACM/IEEE International Conference on Cyber-Physical Systems , ser. ICCPS ’18, 2018, p. 140–149
2018
Cited alongside, same era.
M. Bujarbaruah, X. Zhang, and F. Borrelli, “Adaptive MPC with chance constraints for FIR systems,” in 2018 Annual American Control Conference (ACC) , June 2018, pp. 2312–2317
2018
Cited alongside, same era.
M. Ott, M. Auli, D. Grangier, and M. Ranzato, “Analyzing uncertainty in neural machine translation,” 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
L. Hewing, J. Kabzan, and M. N. Zeilinger, “Cautious model predictive control using gaussian process regression,” IEEE Transactions on Control Systems Technology , 2019
2019
Later among the works it cites.
W. Zhi, T. Lai, L. Ott, G. Francis, and F. Ramos, “Octnet: Trajectory generation in new environments from past experiences,” 2019
2019
Later among the works it cites.
T. Fitzgerald, E. Short, A. Goel, and A. Thomaz, “Human-guided trajectory adaptation for tool transfer,” in Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems , ser. AAMAS ’19, 2019, pp. 1350–1358
2019
Later among the works it cites.
V. T. Vasudevan, A. Sethy, and A. R. Ghias, “Towards better confidence estimation for neural models,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 7335–7339
2019
Later among the works it cites.
C. Vallon and F. Borrelli, “Task decomposition for iterative learning model predictive control,” in 2020 American Control Conference (ACC) . IEEE, 2020
2020
Later among the works it cites.
D. Zhang, Z. Wang, and T. Masayoshi, “Neural-network-based iterative learning control for multiple tasks,” IEEE Transactions on Neural Networks and Learning Systems , 2020
2020
Later among the works it cites.
K. Patan and M. Patan, “Neural-network-based iterative learning control of nonlinear systems,” ISA transactions , vol. 98, pp. 445–453, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
Z. Wang and R. M. Jungers, “Data-driven computation of invariant sets of discrete time-invariant black-box systems,” 2020
2020
Later among the works it cites.
U. Rosolia, A. Singletary, and A. D. Ames, “Unified multi-rate control: from low level actuation to high level planning,” 2020
2020
Later among the works it cites.
A. A. Armstrong and A. G. Alleyne, “A multi-input single-output iterative learning control for improved material placement in extrusion-based additive manufacturing,” Control Engineering Practice , vol. 111, p. 104783, 2021
2021
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