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CS 699: Formal Methods for Robotics, Schedule

Date Deadline Topic Reading Material Presenter
1/25 -- Course Overview, Project Discussion Lecture notes Jyo Deshmukh
2/1 -- Introduction to Formal Verification Baier, Katoen, Larsen, Principles of Model Checking: Ch. 1, Ch. 3 (3.1-3.4) Jared Coleman
J. Deshmukh, S. Sankaranarayanan, Formal Techniques for Verification and Testing of Cyber-Physical Systems (Sec. 1-5) Jingbo Wang
E. Bartocci, et al., Specification-based monitoring of cyber-physical systems: a survey on theory, tools and applications (pp. 136-161) Sheryl Paul
2/8 -- Specifications C. Menghi, et al., Specification patterns for robotic missions Aniruddh Puranic
H. Kress-Gazit, G. Fainekos, G. Pappas, Translating structured english to robot controllers Hejia Zhang
M. Luckcuck et al., Formal Specification and Verification of Autonomous Robotic Systems: A Survey Yannan Li
2/15 -- Statistical Model Checking G. Agha, K. Palmskog, A Survey of Statistical Model Checking Yuan Xia
P. Ashok, J. Křetínský, M. Weininger, PAC Statistical Model Checking for Markov Decision Processes and Stochastic Games Shao-Hung Chan
M. Zarei, Y. Wang, M. Pajic, Statistical Verification of Learning-Based Cyber-Physical Systems Xin Qin
2/22 -- Probabilistic Model Checking C. Baier, H. Hermanns, J.-P. Katoen, The 10,000 Facetsof MDP Model Checking Jared Coleman
G. Norman, D. Parker, J. Sproston, Model checking for probabilistic timed automata Anand Balakrishnan
J.-P. Katoen, The Probabilistic Model Checking Landscape Jingbo Wang
3/1 Skeleton Due Control Theory & Robotics X. Xu et al., Robustness of Control Barrier Functions for Safety Critical Control Anand Balakrishnan
L. Wang, A. Ames, M. Egerstedt, Safety Barrier Certificates for Collisions-Free Multirobot Systems Jared Coleman
J. Cortés, M. Egerstedt, Coordinated Control of Multi-Robot Systems: A Survey Sheryl Paul
3/8 -- Reactive Synthesis & Robotics R. Bloem, K. Chatterjee, B. Jobstmann, Graph Games and Reactive Synthesis Yannan Li
H. Kress-Gazit, M. Lahijanian, V. Raman, Synthesis for Robots: Guarantees and Feedback for Robot Behavior Aniruddh Puranic
K. Chatterjee et al., Qualitative analysis of POMDPs with temporal logic specifications for robotics applications Hejia Zhang
3/15 Reviews Due Temporal Logic & Planning Lahijanian et al., This Time the Robot Settles for a Cost: A Quantitative Approach to Temporal Logic Planning with Partial Satisfaction Xin Qin
G. Fainekos et al., Temporal logic motion planning for dynamic robots Anand Balakrishnan
M. Guo, M. Zavlanos, Probabilistic Motion Planning Under Temporal Tasks and Soft Constraints Shao-Hung Chan
3/22 -- Dynamics-aware/Reachability-based Planning J. Ding et al., Reachability-based Synthesis of Feedback Policies for Motion Planning Under Bounded Disturbances Sheryl Paul
J. Chen et al., Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances Yannan Li
Y. Pant et al., Fly-by-Logic: Control of Multi-Drone Fleets with Temporal Logic Objectives Jingbo Wang
3/29 -- Safe Reinforcement Learning: I M. Wen, U. Topcu, Constrained Cross-Entropy Method for Safe Reinforcement Learning Yuan Xia
M. Turchetta et al., Safe Reinforcement Learning via Curriculum Induction Aniruddh Puranic
4/5 Almost-Paper Due Safe Reinforcement Learning: II N. Fulton, A. Platzer, Verifiably Safe Off-Model Reinforcement Learning Xin Qin
N. Jansen et al., Safe Reinforcement Learning Using Probabilistic Shields Jingbo Wang
4/12 -- Reinforcement Learning from Temporal Logic, Reward Shaping E. Hahn et al., Omega-Regular Objectives in Model-Free Reinforcement Learning A. Balakrishnan
A. Bozkurt et al., Model-Free Reinforcement Learning for Stochastic Games with Linear Temporal Logic Objectives Sheryl Paul
C. Neary et al., Reward Machines for Cooperative Multi-Agent Reinforcement Learning Hejia Zhang
4/19 Reviews Due Guarantees in Reinforcement Learning G. Anderson et al., Neurosymbolic Reinforcement Learning with Formally Verified Exploration Jingbo Wang
Lütjens et al., Certified adversarial robustness for deep reinforcement learning Shao-Hung Chan
E. Bacci, D. Parker, Probabilistic Guarantees for Safe Deep Reinforcement Learning Xin Qin
4/26 -- Learning (Policies, Specs) from Demonstrations D. Kasenberg et al., Interpretable apprenticeship learning with temporal logic specifications Yannan Li
M. Vazquez-Chanlatte et al., Learning Task Specifications from Demonstrations Jared Coleman
D. Brown et al., Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences Yuan Xia
5/10 Final Paper Due Project Presentations Everyone