The human–robot interaction (HRI) community often highlights the social context of an interaction as a key consideration when designing, implementing, and evaluating robot behavior. Unfortunately, researchers use the term “social context” in varied ways. This can lead to miscommunication, making it challenging to draw connections between related work on understanding and modeling the social contexts of human–robot interactions. To address this gap, we survey the HRI literature for existing definitions and uses of the term “social context.” Then, we propose a conceptual model for describing the social context of a human–robot interaction. We apply this model to existing work, and we discuss a range of attributes of social contexts that can help researchers plan for interactions, develop behavior models for robots, and gain insights after interactions have taken place. We conclude with a discussion of open research questions in relation to understanding and modeling the social contexts of human–robot interactions.

The development of software agents that can autonomously take actions to achieve goals has been a long-standing foundational objective in the field of AI. Recent advances in generative AI have given rise to a new class of agents. These advances have opened up the possibility of developing agents that can augment knowledge work in finance that primarily involves the cognitive processing of information by skilled humans. In this article, we bring these fields together. We break down the specific challenges in knowledge work in finance, review the current literature on generative AI agents, and identify potential directions for research and development that would help realize the potential of generative AI agents in finance. We conclude by proposing a framework to delineate the levels of autonomy for AI agents in the context of knowledge work, and an architecture for human–AI collaboration that can pave the path for progressively increasing the autonomy of agents.

This article reviews two techniques that use delay for control: time-delay approaches to control problems (which initially may be free of delays) and the intentional insertion of delays into the feedback. We begin with a now widely used time-delay approach to sampled-data control. In networked control systems with communication constraints, this is the only method that accommodates transmission delays larger than the sampling intervals. We present a predictor-based design that enlarges the maximum allowable delay, which is important for practical implementations. We then discuss methods that use artificial delays via simple Lyapunov functionals that lead to feasible linear matrix inequalities for small delays and simple sampled-data implementations. Finally, we briefly present a new time-delay approach—this time to averaging. Unlike previous results, this approach provides the first quantitative bounds on the small parameter, making averaging-based control (including vibrational and extremum-seeking control) reliable.

This article reviews the literature on urban air mobility (UAM), examining both the research challenges it presents and the transformative opportunities that make these challenges worth addressing. While UAM has historical precedents, the current iteration is born of novel aircraft technology, primarily electric vertical takeoff and landing (eVTOL) and electric short takeoff and landing (eSTOL) aircraft. These advances raise new questions in aerodynamics, control, and integration with urban infrastructure. We explore several key research areas, including aircraft design, vertiport development, network planning, and air traffic management. We also address the scalability challenges in air traffic management for high-density UAM operations and the potential of autonomous and remotely piloted systems. If new aircraft are to birth a new urbanism, they will do so by integrating aircraft engineering and computational intelligence in control, systems, robotics, and human factors.

This review deals with the control of parabolic trough collector (PTC) solar power plants. After a brief introduction, we present a description of PTC plants. We then provide a short literature review and describe some of our experiences. We also describe new control trends in PTC plants. Recent research has focused on (a) new control methods using mobile sensors mounted on drones and unmanned ground vehicles as an integral part of the control systems; (b) spatially distributed solar irradiance estimation methods using a variable fleet of sensors mounted on drones and unmanned ground vehicles; (c) strategies to achieve thermal balance in large-scale fields; (d) new model predictive control algorithms using mobile solar sensor estimates and predictions for safer and more efficient plant operation, which allow the effective integration of solar energy and combine coalitional and artificial intelligence techniques; and (e) fault detection and diagnosis methods to ensure safe operation.

Focus on the ethics of a given technology tends to lag far behind its development. This lag has been particularly acute in the case of artificial intelligence, whose accelerated deployment in a wide range of domains has triggered unprecedented attention on the risks and consequences for society at large, leading to a myriad of ethics regulations, which are difficult to coordinate and integrate due to their late appearance. The very nature of social robots forces their deployment to occur at a much slower pace, providing an opportunity for a profound reflection on ethics, which is already happening in multidisciplinary teams. This article provides a personal view of the ethics landscape, centered on the particularities of social robotics, with the main issues being ordered along two axes (individual and societal) and grouped into eight categories (human dignity, human autonomy, robot transparency, emotional bonding, privacy and safety, justice, freedom, and responsibility). This structure stems from the experience of developing and teaching a university course on ethics in social robotics, whose pedagogical materials are freely available.

While noise is generally associated with uncertainties and often has a negative connotation in engineering, living organisms have evolved to adapt to (and even exploit) such uncertainty to ensure the survival of a species or implement certain functions that would have been difficult or even impossible otherwise. In this article, we review the role and impact of noise in systems and synthetic biology, with a particular emphasis on its role in the genetic control of biological systems, an area we refer to as cybergenetics. The main modeling paradigm is that of stochastic reaction networks, whose applicability goes beyond biology, as these networks can represent any population dynamics system, including ecological, epidemiological, and opinion dynamics networks. We review different ways to mathematically represent these systems, and we notably argue that the concept of ergodicity presents a particularly suitable way to characterize their stability. We then discuss noise-induced properties and show that noise can be both an asset and a nuisance in this setting. Finally, we discuss recent results on (stochastic) cybergenetics and explore their relationships to noise. Along the way, we detail the different technical and biological constraints that need to be respected when designing synthetic biological circuits. Finally, we discuss the concepts, problems, and solutions exposed in the article; raise criticisms and concerns about current ideas and approaches; suggest current (open) problems with potential solutions; and provide some ideas for future research directions.

In this article, we provide a control-theoretic perspective on the research area of behavior trees in robotics. The key idea underlying behavior trees is to make use of modularity, hierarchies, and feedback in order to handle the complexity of a versatile robot control system. Modularity is a well-known tool to handle software complexity by enabling the development, debugging, and extension of separate modules without detailed knowledge of the entire system. A hierarchy of such modules is natural, since robot tasks can often be decomposed into a hierarchy of subtasks. Finally, feedback control is a fundamental tool for handling uncertainties and disturbances in any low-level control system, but in order to enable feedback control on the higher level, where one module decides what submodule to execute, information regarding the progress and applicability of each submodule needs to be shared in the module interfaces. We describe how these three concepts can be used in theoretical analysis, practical design, and extensions and combinations with other ideas from control theory and robotics.

Enzymes are appealing diagnostic targets because of their centrality in human health and disease. Continuous efforts spanning several decades have yielded methods for magnetically detecting the interactions of enzymes with exogenous molecular substrates. Nevertheless, measuring enzymatic activity in vivo remains challenging due to background noise, insufficient selectivity, and overlapping enzymatic functions. Magnetic micro- and nanoagents are poised to help overcome these issues by offering possible advantages such as site-selective sampling, modular architectures, new forms of magnetic detection, and favorable biocompatibility. Here, we review relevant control and detection strategies and consider examples of magnetic enzyme detection demonstrated with micro- or nanorobotic systems. Most cases have focused on proteolytic enzymes, leaving ample opportunity to expand to other classes of enzymes. Enzyme-responsive magnetic micro- and nanoagents hold promise for lowering barriers of translation and enabling preemptive, point-of-care medical applications.

Internal models are nowadays customarily used in different domains of science and engineering to describe how living organisms or artificial computational units embed their acquired knowledge about recurring events taking place in the surrounding environment. This article reviews the internal model principle in control theory, bioengineering, and neuroscience, illustrating the fundamental concepts and theoretical developments of the few last decades of research.