Safe Model-based Control from Signal Temporal Logic Specifications Using Recurrent Neural Networks

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997

1997

Earlier work this paper cites.

O. Maler and D. Nickovic, “Monitoring temporal properties of continuous signals,” in Formal Techniques, Modelling and Analysis of Timed and Fault-Tolerant Systems . Springer, 2004, pp. 152–166

2004

Earlier work this paper cites.

N. Koenig and A. Howard, “Design and use paradigms for gazebo, an open-source multi-robot simulator,” in 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566) , vol. 3. IEEE, 2004, pp. 2149–2154

2004

Earlier work this paper cites.

C. Baier and J.-P. Katoen, Principles of model checking . MIT press, 2008

2008

Earlier work this paper cites.

A. Donzé and O. Maler, “Robust satisfaction of temporal logic over real-valued signals,” in International Conference on Formal Modeling and Analysis of Timed Systems . Springer, 2010, pp. 92–106

2010

Earlier work this paper cites.

M. Deisenroth and C. E. Rasmussen, “Pilco: A model-based and data-efficient approach to policy search,” in Proceedings of the 28th International Conference on machine learning (ICML-11) . Citeseer, 2011, pp. 465–472

2011

Earlier work this paper cites.

V. Raman, A. Donzé, M. Maasoumy, R. M. Murray, A. Sangiovanni-Vincentelli, and S. A. Seshia, “Model predictive control with signal temporal logic specifications,” in 53rd IEEE Conference on Decision and Control . IEEE, 2014, pp. 81–87

2014

Earlier work this paper cites.

A. Dokhanchi, B. Hoxha, and G. Fainekos, “On-line monitoring for temporal logic robustness,” in International Conference on Runtime Verification . Springer, 2014, pp. 231–246

2014

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

Original

2014

Earlier work this paper cites.

S. Sadraddini and C. Belta, “Robust temporal logic model predictive control,” in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton) . IEEE, 2015, pp. 772–779

2015

Earlier work this paper cites.

D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané, “Concrete problems in ai safety,” arXiv preprint arXiv:1606.06565 , 2016

Original

2016

Earlier work this paper cites.

D. Aksaray, A. Jones, Z. Kong, M. Schwager, and C. Belta, “Q-learning for robust satisfaction of signal temporal logic specifications,” in 2016 IEEE 55th Conference on Decision and Control (CDC) . IEEE, 2016, pp. 6565–6570

2016

Earlier work this paper cites.

Y. Gal, R. McAllister, and C. E. Rasmussen, “Improving pilco with bayesian neural network dynamics models,” in Data-Efficient Machine Learning workshop, ICML , vol. 4, no. 34, 2016, p. 25

2016

Earlier work this paper cites.

Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning . PMLR, 2016, pp. 1050–1059

2016

Cited alongside, same era.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning , 2016, vol. 1, no. 2

2016

Cited alongside, same era.

Y. V. Pant, H. Abbas, and R. Mangharam, “Smooth operator: Control using the smooth robustness of temporal logic,” in 2017 IEEE Conference on Control Technology and Applications (CCTA) . IEEE, 2017, pp. 1235–1240

2017

Cited alongside, same era.

A. Agrawal and K. Sreenath, “Discrete control barrier functions for safety-critical control of discrete systems with application to bipedal robot navigation.” in Robotics: Science and Systems , 2017

2017

Cited alongside, same era.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017

Y. Gilpin, V. Kurtz, and H. Lin, “A smooth robustness measure of signal temporal logic for symbolic control,” IEEE Control Systems Letters , vol. 5, no. 1, pp. 241–246, 2020

2020

Later among the works it cites.

S. Yaghoubi and G. Fainekos, “Worst-case satisfaction of stl specifications using feedforward neural network controllers: a lagrange multipliers approach,” in 2020 Information Theory and Applications Workshop (ITA) . IEEE, 2020, pp. 1–20

2020

Later among the works it cites.

M. Ma, J. Gao, L. Feng, and J. Stankovic, “Stlnet: Signal temporal logic enforced multivariate recurrent neural networks,” Advances in Neural Information Processing Systems , vol. 33, pp. 14 604–14 614, 2020

2020

Later among the works it cites.

H. Venkataraman, D. Aksaray, and P. Seiler, “Tractable reinforcement learning of signal temporal logic objectives,” in Learning for Dynamics and Control . PMLR, 2020, pp. 308–317

2020

Later among the works it cites.