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The University of New Mexico

2021

  1. A. J. Thorpe, K. R. Ortiz, and M. M. K. Oishi, “SReachTools Kernel Module: Data-Driven Stochastic Reachability Using Hilbert Space Embeddings of Distributions," HSCC 2021, DOI: 10.24433/CO.7737058.v1

  2. D. Raju, R. Ehlers, and U. Topcu, “Adapting to the Behavior of Environments with Bounded Memory”, Electronic Proceedings in Theoretical Computer Science, 2021, vol. 346, pp. 52–66, doi: 10.4204/EPTCS.346.4.

  3. S. Byeon, W. Jin, D. Sun and I. Hwang, "Human-Automation Interaction for Assisting Novices to Emulate Experts by Inferring Task Objective Functions", 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), 2021, pp. 1-6, doi: 10.1109/DASC52595.2021.9594324.

  4. M. S. Luster an B. J. Pitts, “A Preliminary Investigation into Learning Behaviors in Complex Environments for Human-in-the-Loop Cyber-Physical Systems”, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2021, vol. 65, p. 1, doi: doi.org/10.1177/1071181321651222.

  5. A. J. Thorpe and M. M. K. Oishi, "Stochastic Optimal Control via Hilbert Space Embeddings of Distributions", 2021 60th IEEE Conference on Decision and Control (CDC), 2021, pp. 904-911, doi: 10.1109/CDC45484.2021.9682801.

  6. Karabag, M. O., Neary, C., & Topcu, U. (2021). “Smooth Convex Optimization Using Sub-Zeroth-Order Oracles. Proceedings of the AAAI Conference on Artificial Intelligence”, 35(5), 3815-3822, doi: 10.1609/aaai.v35i5.16499

  7. K. Watanabe, N. Renninger, S. Sankaranarayanan and M. Lahijanian, "Probabilistic Specification Learning for Planning with Safety Constraints", 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 6558-6565, doi: 10.1109/IROS51168.2021.9636712.

  8. A. J. Thorpe, V. Sivaramakrishnan and M. M. K. Oishi, "Approximate Stochastic Reachability for High Dimensional Systems", 2021 American Control Conference (ACC), 2021, pp. 1287-1293, doi: 10.23919/ACC50511.2021.9483404.

  9. B. He, M. Ghasemi, U. Topcu and L. Sentis, "A Barrier Pair Method for Safe Human-Robot Shared Autonomy", 2021 60th IEEE Conference on Decision and Control (CDC), 2021, pp. 2854-2861, doi: 10.1109/CDC45484.2021.9682961.

  10. S. Byeon, W. Jin, D. Sun and I. Hwang, "Human-Automation Interaction for Assisting Novices to Emulate Experts by Inferring Task Objective Functions", 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), 2021, pp. 1-6, doi: 10.1109/DASC52595.2021.9594324.

  11. S. Byeon, D. Sun and I. Hwang, "Skill-level-based Hybrid Shared Control for Human-Automation Systems", 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021, pp. 1507-1512, doi: 10.1109/SMC52423.2021.9658994.

  12. M.S. Luster and B.J. Pitts (2021). “A Preliminary Investigation into Learning Behaviors in Complex Environments for Human-in-the-Loop Cyber-Physical Systems”, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 65(1), 42–46, doi: 10.1177/1071181321651222.

  13. Y. Chou, H. Yoon and S. Sankaranarayanan, "Predictive Runtime Monitoring of Vehicle Models Using Bayesian Estimation and Reachability Analysis," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 2111-2118, doi: 10.1109/IROS45743.2020.9340755.

  14. M. Ghasemi, E. Scope Crafts, B. Zhao, and U. Topcu, “Multiple Plans are Better than One: Diverse Stochastic Planning”, ICAPS, vol. 31, no. 1, pp. 140-148, May 2021, doi: https://doi.org/10.1609/icaps.v31i1.15956.