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1. Secure Control Under Partial Observability with Temporal Logic Constraints

   https://doi.org/https://doi.org/10.23919/ACC.2019.8814630  

   Ramasubramanian, B; Clark, A; Bushnell, L; Poovendran, R. (January 2019, American Control Conference (ACC))

   This paper studies the synthesis of control policies for an agent that has to satisfy a temporal logic specification in a partially observable environment, in the presence of an adversary. The interaction of the agent (defender) with the adversary is modeled as a partially observable stochastic game. The search for policies is limited to over the space of finite state controllers, which leads to a tractable approach to determine policies. The goal is to generate a defender policy to maximize satisfaction of a given temporal logic specification under any adversary policy. We relate the satisfaction of the specification in terms of reaching (a subset of) recurrent states of a Markov chain. We then present a procedure to determine a set of defender and adversary finite state controllers of given sizes that will satisfy the temporal logic specification. We illustrate our approach with an example. 

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2. Optimization-based Task and Motion Planning under Signal Temporal Logic Specifications using Logic Network Flow

   Lin, Xuan; Ren, Jiming; Coogan, Samuel; Zhao, Ye (May 2025, IEEE International Conference on Robotics and Automation)

   This paper proposes an optimization-based task and motion planning framework, named “Logic Network Flow”, to integrate signal temporal logic (STL) specifications into efficient mixed-binary linear programmings. In this framework, temporal predicates are encoded as polyhedron constraints on each edge of the network flow, instead of as constraints between the nodes as in the traditional Logic Tree formulation. Synthesized with Dynamic Network Flows, Logic Network Flows render a tighter convex relaxation compared to Logic Trees derived from these STL specifications. Our formulation is evaluated on several multi-robot motion planning case studies. Empirical results demonstrate that our formulation outperforms Logic Tree formulation in terms of computation time for several planning problems. As the problem size scales up, our method still discovers better lower and upper bounds by exploring fewer number of nodes during the branch-andbound process, although this comes at the cost of increased computational load for each node when exploring branches. 

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3. Temporal Logic Guided Locomotion Planning and Control in Cluttered Environments

   Kulgod, Sutej; Chen, Wentao; Huang, Junda; Zhao, Ye; Atanasov, Nikolay (July 2020, 2020 American Control Conference)

   We present planning and control techniques for non-periodic locomotion tasks specified by temporal logic in rough cluttered terrains. Our planning approach is based on a discrete set of motion primitives for the center of mass (CoM) of a general bipedal robot model. A deterministic shortest path problem is solved over the Bu ̈chi automaton of the temporal logic task specification, composed with the graph of CoM keyframe states generated by the motion primitives. A low-level controller based on quadratic programming is proposed to track the resulting CoM and foot trajectories. We demonstrate dynamically stable, non-periodic locomotion of a kneed compass gait bipedal robot satisfying complex task specifications. 

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4. Falsification and Control of CPS using the Language Set of Discrete-Time Temporal Logic

   https://doi.org/10.1145/3716550.3722040  

   Abou-Mrad, Christian; Khandait, Tanmay; Pedrielli, Giulia; Abbas, Houssam (May 2025, ACM)

   We present algorithms for Cyber-Physical Systems (CPS) falsification and control, which take advantage of knowing the entire language of the temporal logic specification - that is, the set of signals that satisfy the formula. In the design of CPS, falsification and control play key roles. Falsification is a testing task, where the goal is to find an input signal that causes the system's output trajectory to violate the correctness requirements. Control is the dual task, where the goal is to find an input signal that causes the system's output to satisfy the specification. When the specification is expressed in a temporal logic, most existing work relies on local optimization heuristics to perform both tasks. In this paper, we explore whether a different expression of the specification offers advantages when performing falsification and control. Recent work presented a method for computing a representation of the language of a formula in (discrete-time) Signal Temporal Logic (STL), showing that the language can be represented as a union of polytopes. We introduce new falsification algorithms which combine distance information to the different components of the language to accelerate the convergence to a falsifier. And we introduce a new algorithm for computing a satisfying control signal which works by repeatedly projecting violating output trajectories back onto the language's components. Moreover, these algorithms are trivially parallelizable to take advantage of multiple processors. Despite their relative simplicity, our algorithms demonstrate 10x to 100x speedups relative to the state-of-the-art. 

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5. Predictive Runtime Monitoring for Mobile Robots using Logic-Based Bayesian Intent Inference

   https://doi.org/10.1109/ICRA48506.2021.9561193  

   Yoon, Hansol; Sankaranarayanan, Sriram (May 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA))

   We propose a predictive runtime monitoring framework that forecasts the distribution of future positions of mobile robots in order to detect and avoid impending property violations such as collisions with obstacles or other agents. Our approach uses a restricted class of temporal logic formulas to represent the likely intentions of the agents along with a combination of temporal logic-based optimal cost path planning and Bayesian inference to compute the probability of these intents given the current trajectory of the robot. First, we construct a large but finite hypothesis space of possible intents represented as temporal logic formulas whose atomic propositions are derived from a detailed map of the robot’s workspace. Next, our approach uses real-time observations of the robot’s position to update a distribution over temporal logic formulae that represent its likely intent. This is performed by using a combination of optimal cost path planning and a Boltzmann noisy rationality model. In this manner, we construct a Bayesian approach to evaluating the posterior probability of various hypotheses given the observed states and actions of the robot. Finally, we predict the future position of the robot by drawing posterior predictive samples using a Monte-Carlo method. We evaluate our framework using two different trajectory datasets that contain multiple scenarios implementing various tasks. The results show that our method can predict future positions precisely and efficiently, so that the computation time for generating a prediction is a tiny fraction of the overall time horizon. 

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