Diffusion-Guided Model Predictive Control for Signal Temporal Logic Specifications

We present a general framework for risk semantics on Signal Temporal Logic (STL) specifications for stochastic dynamical systems using axiomatic risk theory. We show that under our recursive risk semantics, risk constraints on STL formulas can be expressed in terms of risk constraints on atomic predicates. We then show how this allows a (stochastic) STL risk constraint to be transformed into a risk-tightened deterministic STL constraint on a related deterministic nominal system, enabling the application of existing STL methods. For affine predicate functions and a (coherent) Distributionally Robust Value at Risk measure, we show how risk constraints on atomic predicates can be reformulated as tightened deterministic affine constraints. We demonstrate the framework using a Model Predictive Control (MPC) design with an STL risk constraint.

We consider the problem of controlling a system with unknown, stochastic dynamics to achieve a complex, time-sensitive task. An example of this problem is controlling a noisy aerial vehicle with partially known dynamics to visit a pre-specified set of regions in any order while avoiding hazardous areas. In particular, we are interested in tasks which can be described by signal temporal logic (STL) specifications. STL is a rich logic that can be used to describe tasks involving bounds on physical parameters, continuous time bounds, and logical relationships over time and states. STL is equipped with a continuous measure called the robustness degree that measures how strongly a given sample path exhibits an STL property [4, 3]. This measure enables the use of continuous optimization problems to solve learning [7, 6] or formal synthesis problems [9] involving STL.

This paper investigates the problem of safety-critical dynamic event-triggered control for nonlinear systems subject to signal temporal logic (STL) specifications. A novel event-triggered controller is proposed by integrating high-order control barrier functions (HOCBFs) with a dynamic event-triggered mechanism (DETM) to ensure STL specification satisfaction. Unlike conventional safety-critical control strategies that rely on continuous-time feedback, the proposed controller significantly reduces the demand for communication and computational resources while preventing the occurrence of the Zeno phenomenon. Finally, simulation results are provided to demonstrate the effectiveness of the proposed method.

Sampling-based Motion Planning with Temporal Logic Missions and Spatial Preferences

Deep Reinforcement Learning (DRL) has the potential to be used for synthesizing feedback controllers (agents) for various complex systems with unknown dynamics. These systems are expected to satisfy diverse safety and liveness properties best captured using temporal logic. In RL, the reward function plays a crucial role in specifying the desired behaviour of these agents. However, the problem of designing the reward function for an RL agent to satisfy complex temporal logic specifications has received limited attention in the literature. To address this, we provide a systematic way of generating rewards in real-time by using the quantitative semantics of Signal Temporal Logic (STL), a widely used temporal logic to specify the behaviour of cyber-physical systems. We propose a new quantitative semantics for STL having several desirable properties, making it suitable for reward generation. We evaluate our STL-based reinforcement learning mechanism on several complex continuous control benchmarks and compare our STL semantics with those available in the literature in terms of their efficacy in synthesizing the controller agent. Experimental results establish our new semantics to be the most suitable for synthesizing feedback controllers for complex continuous dynamical systems through reinforcement learning.

What is the frequency content of temporal logic formulas? That is, when we monitor a signal against a formula, which frequency bands of the signal are relevant to the logic and should be preserved, and which can be safely discarded? This question is relevant whenever signals are filtered or compressed before being monitored, which is almost always the case for analog signals. To answer this question, we focus on monitors that measure the robustness of a signal relative to a specification in Signal Temporal Logic. We prove that robustness monitors can be modeled using Volterra series. We then study the Fourier transforms of these Volterra representations, and provide a method to derive the Fourier transforms of entire formulas. We also make explicit the measurement process in temporal logic and re-define it on the basis of distributions to make it compatible with measurements in signal processing. Experiments illustrate these results. Beyond compression, this work enables the integration of temporal logic monitoring into common signal processing tool chains as just another signal processing operation, and enables a common formalism to study both logical and non-logical operations in the frequency domain, which we refer to as Logical Signal Processing.

Uncertainty in safety-critical cyber-physical systems can be modeled using a finite number of parameters or input signals. Given a system specification in Signal Temporal Logic (STL), we would like to verify that for all (infinite) values of the model parameters/input signals, the system satisfies its specification. Unfortunately, this problem is undecidable in general. Statistical model checking (SMC) offers a solution by providing guarantees on the correctness of CPS models by statistically reasoning on model simulations. We propose a new approach for statistical verification of CPS models for user-provided distribution on the model parameters. Our technique uses model simulations to learn surrogate models, and uses conformal inference to provide probabilistic guarantees on the satisfaction of a given STL property. Additionally, we can provide prediction intervals containing the quantitative satisfaction values of the given STL property for any user-specified confidence level. We also propose a refinement procedure based on Gaussian Process (GP)-based surrogate models for obtaining fine-grained probabilistic guarantees over sub-regions in the parameter space. This in turn enables the CPS designer to choose assured validity domains in the parameter space for safety-critical applications. Finally, we demonstrate the efficacy of our technique on several CPS models.

We present a controller for human-robot handovers that is automatically synthesized from high-level specifications in Signal Temporal Logic (STL). In contrast to existing controllers, this approach can provide formal guarantees on the timing of each of the handover phases. Using synthesis also allows end-users to specify and dynamically change the robot's behaviors using high-level requirements of goals and constraints rather than by tuning low-level controller parameters. We illustrate the proposed approach by replicating the behavior of existing handover strategies from the literature. We also identify specification parameters that are likely to lead to successful handovers using a public database of human-human handovers.

This paper proposes a novel version of the vehicle routing problem (VRP). Instead of servicing all the customers, feasible solutions of the VRP instance are forced to satisfy a set of complex high-level tasks given as a metric temporal logic (MTL) specification, which allows complex quantitative timing constraints to be incorporated into the problem. For the resulting vehicle routing problem with metric temporal logic specifications (VRPMTL), a mixed-integer linear programming (MILP) based algorithm is provided that solves the problem to optimality. Examples for optimal multi-UAV mission planning are provided where MTL is used as a high level language to specify complex mission tasks.

We develop a distributed motion planner for multi-robot systems with Signal Temporal Logic (STL) objectives. Existing approaches to STL-based motion planning are limited to fragments of STL that might not capture all desired behaviors or safety requirements. Focusing on the case of a fleet of multi-rotor Unmanned Aerial Vehicles (UAVs) jointly tasked with satisfying a given STL mission, we develop a distributed method, Fly-by-Distributed-Logic (FBDL), for trajectory planning. The proposed method generates trajectories that maximize the smooth robustness of STL specifications, where the associated optimization is solved in a distributed manner. This, to the best of our knowledge, is the first distributed method that can handle the full grammar of STL (bounded time). Simulation studies show the applicability of our approach to a variety of STL missions, including those with nested STL operators and the Until operator. We also compare its performance to a centralized approach and show that our method is computationally faster, but generally computes trajectories with lower robustness.

This paper presents a control strategy synthesis method for dynamical systems with differential constraints, emphasizing the prioritization of specific rules. Special attention is given to scenarios where not all rules can be simultaneously satisfied to complete a given task, necessitating decisions on the extent to which each rule is satisfied, including which rules must be upheld or disregarded. We propose a learning-based Model Predictive Control (MPC) method designed to address these challenges. Our approach integrates a learning method with a traditional control scheme, enabling the controller to emulate human expert behavior. Rules are represented as Signal Temporal Logic (STL) formulas. A robustness margin, quantifying the degree of rule satisfaction, is learned from expert demonstrations using a Conditional Variational Autoencoder (CVAE). This learned margin is then applied in the MPC process to guide the prioritization or exclusion of rules. In a track driving simulation, our method demonstrates the ability to generate behavior resembling that of human experts and effectively manage rule-based dilemmas.

We propose a framework based on Recurrent Neural Networks (RNNs) to determine an optimal control strategy for a discrete-time system that is required to satisfy specifications given as Signal Temporal Logic (STL) formulae. RNNs can store information of a system over time, thus, enable us to determine satisfaction of the dynamic temporal requirements specified in STL formulae. Given a STL formula, a dataset of satisfying system executions and corresponding control policies, we can use RNNs to predict a control policy at each time based on the current and previous states of system. We use Control Barrier Functions (CBFs) to guarantee the safety of the predicted control policy. We validate our theoretical formulation and demonstrate its performance in an optimal control problem subject to partially unknown safety constraints through simulations.

The fundamental idea of this work is to synthesize reactive controllers such that closed-loop execution trajectories of the system satisfies desired specifications that ensure correct system behaviors, while optimizing a desired performance criteria. We define the correctness of a system's behavior according to the system's relation to the environment, for example, the output trajectories of the system terminate in a goal set without entering an unsafe set. Using Metric Temporal Logic (MTL) specifications we can capture complex system behaviors and timing requirements, such as the output trajectories must pass through a number of way-points within a certain time frame before terminating in the goal set. Given a Mixed Logical Dynamical (MLD) system and system specifications in terms of MTL formula or simpler reach-avoid specifications, our goal is to find a closed-loop trajectory that satisfies the specifications, in non-deterministic environments. Using an MILP framework we search over the space of input signals to obtain such valid trajectories of the system, by adding constraints to satisfy the MTL formula only when necessary, to avoid the exponential complexity of solving MILP problems. We also present experimental results for planning a path in real-time for a mobile robot through a dynamically changing environment with a desired task specification.

In this paper, we investigate fully-automated controller synthesis of coordination strategies for multi-robot warehouse robotic systems. We consider the scenario where a group of automated guided vehicles (AGVs) deliver goods from starting locations to destinations in a grid-abstracted workspace. We propose a distributed framework with prioritized-based coordination strategy to fulfill the entire planning task. Specifically, the task of each AGV is specified by a Linear Temporal Logic (LTL) specification. The local control strategies are obtained by solving the corresponding LTL reactive synthesis problems. In order to avoid conflicts, we use the prioritized-based coordination strategy to guarantee that the overall systems is safe and live, i.e., AGVs will collide with each other and there is no deadlock and livelock. The proposed design methodology is correct-by-construction with the formal guarantee and is amenable to handle temporal tasks in a flexible manner. Furthermore, the proposed framework is distributed; hence computationally more efficient than the centralized approach. Finally, we demonstrate the proposed distributed framework by simulation examples.

Manipulation planning from high-level task specifications, even though highly desirable, is a challenging problem. The large dimensionality of manipulators and complexity of task specifications make the problem computationally intractable. This work introduces a manipulation planning framework with linear temporal logic (LTL) specifications. The use of LTL as the specification language allows the expression of rich and complex manipulation tasks. The framework deals with the state-explosion problem through a novel abstraction technique. Given a robotic system, a workspace consisting of obstacles, manipulable objects, and locations of interest, and a co-safe LTL specification over the objects and locations, the framework computes a motion plan to achieve the task through a synergistic multi-layered planning architecture. The power of the framework is demonstrated through case studies, in which the planner efficiently computes plans for complex tasks. The case studies also illustrate the ability of the framework in intelligently moving away objects that block desired executions without requiring backtracking.