arXiv subjects

Sofie Haesaert

Publications and source records attributed to Sofie Haesaert.

At least 37 records · Page 2Linked to original sources

arXiv2024

SymAware: A Software Development Framework for Trustworthy Multi-Agent Systems with Situational Awareness

Developing trustworthy multi-agent systems for practical applications is challenging due to the complicated communication of situational awareness (SA) among agents. This paper showcases a novel efficient and easy-to-use software framework for multi-agent simulation, named SymAware which provides a rich set of predefined data structures to compute, store, and communicate SA for agents. It also provides an abstract interface for the agents to compute their control inputs taking into account the awareness of the situation, knowledge, and risk of surrounding agents. Besides, utilizing a cluster of specialized components, SymAware hides the heavy computation of physical rendering and communication interfacing of simulation engines behind the control threads, resulting in high implementation efficiency in bridging the gap between conceptual prototyping and practical applications. Three multi-agent case studies are used to validate the efficacy and efficiency of this software framework.

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arXiv2024

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with real-time allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into sub-specifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing.

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arXiv2024

Intention-Aware Control Based on Belief-Space Specifications and Stochastic Expansion

This paper develops a correct-by-design controller for an autonomous vehicle interacting with opponent vehicles with unknown intentions. We define an intention-aware control problem incorporating epistemic uncertainties of the opponent vehicles and model their intentions as discrete-valued random variables. Then, we focus on a control objective specified as belief-space temporal logic specifications. From this stochastic control problem, we derive a sound deterministic control problem using stochastic expansion and solve it using shrinking-horizon model predictive control. The solved intention-aware controller allows a vehicle to adjust its behaviors according to its opponents' intentions. It ensures provable safety by restricting the probabilistic risk under a desired level. We show with experimental studies that the proposed method ensures strict limitation of risk probabilities, validating its efficacy in autonomous driving cases. This work provides a novel solution for the risk-aware control of interactive vehicles with formal safety guarantees.

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arXiv2024

Data-driven Dissipativity Analysis of Linear Parameter-Varying Systems

We derive direct data-driven dissipativity analysis methods for Linear Parameter-Varying (LPV) systems using a single sequence of input-scheduling-output data. By means of constructing a semi-definite program subject to linear matrix inequality constraints based on this data-dictionary, direct data-driven verification of $(Q,S,R)$-type of dissipativity properties of the data-generating LPV system is achieved. Multiple implementation methods are proposed to achieve efficient computational properties and to even exploit structural information on the scheduling, e.g., rate bounds. The effectiveness and trade-offs of the proposed methodologies are shown in simulation studies of academic and physically realistic examples.

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arXiv2024

Specification-guided temporal logic control for stochastic systems: a multi-layered approach

Designing controllers to satisfy temporal requirements has proven to be challenging for dynamical systems that are affected by uncertainty. This is mainly due to the states evolving in a continuous uncountable space, the stochastic evolution of the states, and infinite-horizon temporal requirements on the system evolution, all of which makes closed-form solutions generally inaccessible. A promising approach for designing provably correct controllers on such systems is to utilize the concept of abstraction, which is based on building simplified abstract models that can be used to approximate optimal controllers with provable closeness guarantees. The available abstraction-based methods are further divided into discretization-based approaches that build a finite abstract model by discretizing the continuous space of the system, and discretization-free approaches that work directly on the continuous state space without the need for building a finite space. To reduce the conservatism in the sub-optimality of the designed controller originating from the abstraction step, this paper develops an approach that naturally has the flexibility to combine different abstraction techniques from the aforementioned classes and to combine the same abstraction technique with different parameters. First, we develop a multi-layered discretization-based approach with variable precision by combining abstraction layers with different precision parameters. Then, we exploit the advantages of both classes of abstraction-based methods by extending this multi-layered approach guided by the specification to combinations of layers with respectively discretization-based and discretization-free abstractions. We achieve an efficient implementation that is less conservative and improves the computation time and memory usage. We illustrate the benefits of the proposed multi-layered approach on several case studies.

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arXiv2024

Risk-Aware MPC for Stochastic Systems with Runtime Temporal Logics

This paper concerns the risk-aware control of stochastic systems with temporal logic specifications dynamically assigned during runtime. Conventional risk-aware control typically assumes that all specifications are predefined and remain unchanged during runtime. In this paper, we propose a novel, provably correct model predictive control scheme for linear systems with additive unbounded stochastic disturbances that dynamically evaluates the feasibility of runtime signal temporal logic specifications and automatically reschedules the control inputs accordingly. The control method guarantees the probabilistic satisfaction of newly accepted specifications without sacrificing the satisfaction of the previously accepted ones. The proposed control method is validated by a robotic motion planning case study.

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arXiv2024

Direct data-driven control with signal temporal logic specifications

Most control synthesis methods under temporal logic properties require a model of the system, however, identifying such a model can be a challenging task. In this work, we develop a direct data-driven control synthesis method for temporal logic specifications, which does not require this explicit modeling step, capable of providing certificates for the general class of linear systems. After collecting a single sequence of input-output data from the system, we construct a data-driven characterization of the behavior. Using this characterization, we synthesize a controller, such that the controlled system satisfies a (possibly unbounded) temporal logic specification. The underlying optimization problem is solved by mixed-integer linear programming. We demonstrate the applicability of the results through simulation examples.

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arXiv2024

Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside''

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices connected with projects funded by the EIC Pathfinder Challenge called ``Awareness Inside'', a nonrecurring call for proposals within Horizon Europe designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems.

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arXiv2024

Bayesian Formal Synthesis of Unknown Systems via Robust Simulation Relations

This paper addresses the problem of data-driven computation of controllers that are correct by design for safety-critical systems and can provably satisfy (complex) functional requirements. With a focus on continuous-space stochastic systems with parametric uncertainty, we propose a two-stage approach that decomposes the problem into a learning stage and a robust formal controller synthesis stage. The first stage utilizes available Bayesian regression results to compute robust credible sets for the true parameters of the system. For the second stage, we introduce methods for systems subject to both stochastic and parametric uncertainties. We provide simulation relations for enabling correct-by-design control refinement that are founded on coupling uncertainties of stochastic systems via sub-probability measures. The presented relations are essential for constructing abstract models that are related to not only one model but to a set of parameterized models. The results are demonstrated on three case studies, including a nonlinear and a high-dimensional system.

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arXiv2023

Direct data-driven state-feedback control of general nonlinear systems

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a direct linear parameter-varying (LPV) representation. By applying the LPV extension of the Fundamental Lemma in this velocity domain, a state-feedback controller is directly synthesized to provide asymptotic stability and dissipativity of the velocity-form. By using realization theory, the synthesized controller is realized as a NL state-feedback law for the original unknown NL system with guarantees of universal shifted stability and dissipativity, i.e., stability and dissipativity w.r.t. any (forced) equilibrium point, of the closed-loop behavior. This is achieved by the use of a single sequence of data from the system and a predefined basis function set to span the scheduling map. The applicability of the results is demonstrated on a simulation example of an unbalanced disc.

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arXiv2023

Modularized Control Synthesis for Complex Signal Temporal Logic Specifications

The control synthesis of a dynamic system subject to a signal temporal logic (STL) specification is commonly formulated as a mixed-integer linear/convex programming (MILP/MICP) problem. Solving such a problem is computationally expensive when the specification is long and complex. In this paper, we propose a framework to transform a long and complex specification into separate forms in time, to be more specific, the logical combination of a series of short and simple subformulas with non-overlapping timing intervals. In this way, one can easily modularize the synthesis of a long specification by solving its short subformulas, which improves the efficiency of the control problem. We first propose a syntactic timing separation form for a type of complex specifications based on a group of separation principles. Then, we further propose a complete specification split form with subformulas completely separated in time. Based on this, we develop a modularized synthesis algorithm that ensures the soundness of the solution to the original synthesis problem. The efficacy of the methods is validated with a robot monitoring case study in simulation. Our work is promising to promote the efficiency of control synthesis for systems with complicated specifications.

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arXiv2023

Automated Formation Control Synthesis from Temporal Logic Specifications

In many practical scenarios, multi-robot systems are envisioned to support humans in executing complicated tasks within structured environments, such as search-and-rescue tasks. We propose a framework for a multi-robot swarm to fulfill complex tasks represented by temporal logic specifications. Given temporal logic specifications on the swarm formation and navigation, we develop a controller with runtime safety and convergence guarantees that drive the swarm to formally satisfy the specification. In addition, the synthesized controller will autonomously switch formations as necessary and react to uncontrollable events from the environment. The efficacy of the proposed framework is validated with a simulation study on the navigation of multiple quadrotor robots.

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arXiv2023

Abstracting Linear Stochastic Systems via Knowledge Filtering

In this paper, we propose a new model reduction technique for linear stochastic systems that builds upon knowledge filtering and utilizes optimal Kalman filtering techniques. This new technique will reduce the dimension of the noise disturbance and will allow any controller designed for the reduced model to be refined into a controller for the original stochastic system, while preserving any specification on the output. Although initially the reduced model will be time-varying, a method will be provided with which the reduced model can become time-invariant if it satisfies some minor technical conditions. We present our theoretical findings with an example that supports the proposed framework and illustrates how model reduction and controller refinement of stochastic systems can be achieved. We finish the paper by considering specific examples to analyze both completeness with respect to controller synthesis and model order reduction with respect to the state.

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arXiv2023

Energy-Constrained Active Exploration Under Incremental-Resolution Symbolic Perception

In this work, we consider the problem of autonomous exploration in search of targets while respecting a fixed energy budget. The robot is equipped with an incremental-resolution symbolic perception module wherein the perception of targets in the environment improves as the robot's distance from targets decreases. We assume no prior information about the total number of targets, their locations as well as their possible distribution within the environment. This work proposes a novel decision-making framework for the resulting constrained sequential decision-making problem by first converting it into a reward maximization problem on a product graph computed offline. It is then solved online as a Mixed-Integer Linear Program (MILP) where the knowledge about the environment is updated at each step, combining automata-based and MILP-based techniques. We demonstrate the efficacy of our approach with the help of a case study and present empirical evaluation in terms of expected regret. Furthermore, the runtime performance shows that online planning can be efficiently performed for moderately-sized grid environments.

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arXiv2023

Verifying the Unknown: Correct-by-Design Control Synthesis for Networks of Stochastic Uncertain Systems

In this paper, we present an approach for designing correct-by-design controllers for cyber-physical systems composed of multiple dynamically interconnected uncertain systems. We consider networked discrete-time uncertain nonlinear systems with additive stochastic noise and model parametric uncertainty. Such settings arise when multiple systems interact in an uncertain environment and only observational data is available. We address two limitations of existing approaches for formal synthesis of controllers for networks of uncertain systems satisfying complex temporal specifications. Firstly, whilst existing approaches rely on the stochasticity to be Gaussian, the heterogeneous nature of composed systems typically yields a more complex stochastic behavior. Secondly, exact models of the systems involved are generally not available or difficult to acquire. To address these challenges, we show how abstraction-based control synthesis for uncertain systems based on sub-probability couplings can be extended to networked systems. We design controllers based on parameter uncertainty sets identified from observational data and approximate possibly arbitrary noise distributions using Gaussian mixture models whilst quantifying the incurred stochastic coupling. Finally, we demonstrate the effectiveness of our approach on a nonlinear package delivery case study with a complex specification, and a platoon of cars.

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arXiv2023

Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving

Reinforcement learning (RL) is an effective approach to motion planning in autonomous driving, where an optimal driving policy can be automatically learned using the interaction data with the environment. Nevertheless, the reward function for an RL agent, which is significant to its performance, is challenging to be determined. The conventional work mainly focuses on rewarding safe driving states but does not incorporate the awareness of risky driving behaviors of the vehicles. In this paper, we investigate how to use risk-aware reward shaping to leverage the training and test performance of RL agents in autonomous driving. Based on the essential requirements that prescribe the safety specifications for general autonomous driving in practice, we propose additional reshaped reward terms that encourage exploration and penalize risky driving behaviors. A simulation study in OpenAI Gym indicates the advantage of risk-aware reward shaping for various RL agents. Also, we point out that proximal policy optimization (PPO) is likely to be the best RL method that works with risk-aware reward shaping.

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arXiv2023

Stochastic Model Predictive Control with Dynamic Chance Constraints

This work introduces a stochastic model predictive control scheme for dynamic chance constraints. We consider linear discrete-time systems affected by unbounded additive stochastic disturbance. To synthesize an optimal controller, we solve two subsequent stochastic optimization problems. The first problem concerns finding the maximal feasible probabilities of the dynamic chance constraints. After obtaining the probabilities, the second problem concerns finding an optimal controller using stochastic model predictive control. We solve both stochastic optimization problems by reformulating them into deterministic ones using probabilistic reachable tubes and constraint tightening. We prove that the developed algorithm is recursively feasible and yields closed-loop satisfaction of the dynamic chance constraints. In addition, we will introduce a novel implementation using zonotopes to describe the tightening analytically. Finally, we will end with an example illustrating the method's benefits.

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arXiv2023

SySCoRe: Synthesis via Stochastic Coupling Relations

We present SySCoRe, a MATLAB toolbox that synthesizes controllers for stochastic continuous-state systems to satisfy temporal logic specifications. Starting from a system description and a co-safe temporal logic specification, SySCoRe provides all necessary functions for synthesizing a robust controller and quantifying the associated formal robustness guarantees. It distinguishes itself from other available tools by supporting nonlinear dynamics, complex co-safe temporal logic specifications over infinite horizons and model-order reduction. To achieve this, SySCoRe generates a finite-state abstraction of the provided model and performs probabilistic model checking. Then, it establishes a probabilistic coupling to the original stochastic system encoded in an approximate simulation relation, based on which a lower bound on the satisfaction probability is computed. SySCoRe provides non-trivial lower bounds for infinite-horizon properties and unbounded disturbances since its computed error does not grow linearly in the horizon of the specification. It exploits a tensor representation to facilitate the efficient computation of transition probabilities. We showcase these features on several benchmarks and compare the performance of the tool with existing tools.

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