Reinforcement learning (RL) is a promising method to learn optimal control policies for systems with unknown dynamics. In particular, synthesizing controllers for safety-critical systems based on high-level specifications, such as those expressed in temporal languages like linear temporal logic (LTL), presents a significant challenge in control systems research. Current RL-based methods designed for LTL tasks typically offer only asymptotic guarantees, which provide no insight into the transient performance during the learning phase. While running an RL algorithm, it is crucial to assess how close we are to achieving optimal behavior if we stop learning. In this paper, we present the first regret-free online algorithm for learning a controller that addresses the general class of LTL specifications over Markov decision processes (MDPs) with a finite set of states and actions. We begin by proposing a regret-free learning algorithm to solve infinite-horizon reach-avoid problems. For general LTL specifications, we show that the synthesis problem can be reduced to a reach-avoid problem when the graph structure is known. Additionally, we provide an algorithm for learning the graph structure, assuming knowledge of a minimum transition probability, which operates independently of the main regret-free algorithm.

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.

We present a novel technique for online safety verification of autonomous systems, which performs reachability analysis efficiently for both bounded and unbounded horizons by employing neural barrier certificates. Our approach uses barrier certificates given by parameterized neural networks that depend on a given initial set, unsafe sets, and time horizon. Such networks are trained efficiently offline using system simulations sampled from regions of the state space. We then employ a meta-neural network to generalize the barrier certificates to state space regions that are outside the training set. These certificates are generated and validated online as sound over-approximations of the reachable states, thus either ensuring system safety or activating appropriate alternative actions in unsafe scenarios. We demonstrate our technique on case studies from linear models to nonlinear control-dependent models for online autonomous driving scenarios.

Algorithmic verification of realistic systems to satisfy safety and other temporal requirements has suffered from poor scalability of the employed formal approaches. To design systems with rigorous guarantees, many approaches still rely on exact models of the underlying systems. Since this assumption can rarely be met in practice, models have to be inferred from measurement data or are bypassed completely. Whilst former usually requires the model structure to be known a-priori and immense amounts of data to be available, latter gives rise to a plethora of restrictive mathematical assumptions about the unknown dynamics. In a pursuit of developing scalable formal verification algorithms without shifting the problem to unrealistic assumptions, we employ the concept of barrier certificates, which can guarantee safety of the system, and learn the certificate directly from a compact set of system trajectories. We use conditional mean embeddings to embed data from the system into a reproducing kernel Hilbert space (RKHS) and construct an RKHS ambiguity set that can be inflated to robustify the result w.r.t. a set of plausible transition kernels. We show how to solve the resulting program efficiently using sum-of-squares optimization and a Gaussian process envelope. Our approach lifts the need for restrictive assumptions on the system dynamics and uncertainty, and suggests an improvement in the sample complexity of verifying the safety of a system on a tested case study compared to a state-of-the-art approach.

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 that are connected with projects funded by the EIC Pathfinder Challenge called "Awareness Inside", a nonrecurring call for proposals within Horizon Europe that was 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.

We present a modular approach to \emph{reinforcement learning} (RL) in environments consisting of simpler components evolving in parallel. A monolithic view of such modular environments may be prohibitively large to learn, or may require unrealizable communication between the components in the form of a centralized controller. Our proposed approach is based on the assume-guarantee paradigm where the optimal control for the individual components is synthesized in isolation by making \emph{assumptions} about the behaviors of neighboring components, and providing \emph{guarantees} about their own behavior. We express these \emph{assume-guarantee contracts} as regular languages and provide automatic translations to scalar rewards to be used in RL. By combining local probabilities of satisfaction for each component, we provide a lower bound on the probability of satisfaction of the complete system. By solving a Markov game for each component, RL can produce a controller for each component that maximizes this lower bound. The controller utilizes the information it receives through communication, observations, and any knowledge of a coarse model of other agents. We experimentally demonstrate the efficiency of the proposed approach on a variety of case studies.

Abstraction-based techniques are an attractive approach for synthesizing correct-by-construction controllers to satisfy high-level temporal requirements. A main bottleneck for successful application of these techniques is the memory requirement, both during controller synthesis and in controller deployment. We propose memory-efficient methods for mitigating the high memory demands of the abstraction-based techniques using neural network representations. To perform synthesis for reach-avoid specifications, we propose an on-the-fly algorithm that relies on compressed neural network representations of the forward and backward dynamics of the system. In contrast to usual applications of neural representations, our technique maintains soundness of the end-to-end process. To ensure this, we correct the output of the trained neural network such that the corrected output representations are sound with respect to the finite abstraction. For deployment, we provide a novel training algorithm to find a neural network representation of the synthesized controller and experimentally show that the controller can be correctly represented as a combination of a neural network and a look-up table that requires a substantially smaller memory. We demonstrate experimentally that our approach significantly reduces the memory requirements of abstraction-based methods. For the selected benchmarks, our approach reduces the memory requirements respectively for the synthesis and deployment by a factor of $1.31\times 10^5$ and $7.13\times 10^3$ on average, and up to $7.54\times 10^5$ and $3.18\times 10^4$. Although this reduction is at the cost of increased off-line computations to train the neural networks, all the steps of our approach are parallelizable and can be implemented on machines with higher number of processing units to reduce the required computational time.

We present a scalable, black box, perception-in-the-loop technique to find adversarial examples for deep neural network classifiers. Black box means that our procedure only has input-output access to the classifier, and not to the internal structure, parameters, or intermediate confidence values. Perception-in-the-loop means that the notion of proximity between inputs can be directly queried from human participants rather than an arbitrarily chosen metric. Our technique is based on covariance matrix adaptation evolution strategy (CMA-ES), a black box optimization approach. CMA-ES explores the search space iteratively in a black box manner, by generating populations of candidates according to a distribution, choosing the best candidates according to a cost function, and updating the posterior distribution to favor the best candidates. We run CMA-ES using human participants to provide the fitness function, using the insight that the choice of best candidates in CMA-ES can be naturally modeled as a perception task: pick the top $k$ inputs perceptually closest to a fixed input. We empirically demonstrate that finding adversarial examples is feasible using small populations and few iterations. We compare the performance of CMA-ES on the MNIST benchmark with other black-box approaches using $L_p$ norms as a cost function, and show that it performs favorably both in terms of success in finding adversarial examples and in minimizing the distance between the original and the adversarial input. In experiments on the MNIST, CIFAR10, and GTSRB benchmarks, we demonstrate that CMA-ES can find perceptually similar adversarial inputs with a small number of iterations and small population sizes when using perception-in-the-loop. Finally, we show that networks trained specifically to be robust against $L_\infty$ norm can still be susceptible to perceptually similar adversarial examples.