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- Volume 2, 2019
Annual Review of Control, Robotics, and Autonomous Systems - Volume 2, 2019
Volume 2, 2019
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A Century of Robotic Hands
C. Piazza, G. Grioli, M.G. Catalano, and A. BicchiVol. 2 (2019), pp. 1–32More LessThis article reports on the state of the art of artificial hands, discussing some of the field's most important trends and suggesting directions for future research. We review and group the most important application domains of robotic hands, extracting the set of requirements that ultimately led to the use of simplified actuation schemes and soft materials and structures—two themes that clearly emerge from our examination of developments over the past century. We provide a comprehensive analysis of novel technologies for the design of joints, transmissions, and actuators that enabled these trends. We conclude by discussing some important new perspectives generated by simpler and softer hands and their interaction with other aspects of hand design and robotics in general.
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Escaping Oz: Autonomy in Socially Assistive Robotics
Vol. 2 (2019), pp. 33–61More LessThe field of socially assistive robotics (SAR) aims to supplement the efforts of clinicians, therapists, educators, and caregivers through individualized, socially mediated interventions with robots. SAR is faced with the interdisciplinary challenge to balance sensitive domain needs with current technical limitations. Many researchers in SAR and the broader human–robot interaction community overcome technical barriers by using a Wizard of Oz approach, or teleoperation of the robot or aspects of the interaction. Although Wizard of Oz is a well-established practice, it becomes intractable in critical SAR domains that require long-term, situated support, such as aging in place and special needs education. In this article, we define a set of autonomy-centric design properties for SAR interventions based on concepts from artificial intelligence and robotics. These properties structure a systematic review of the last decade of autonomous SAR research. From the review, we draw and discuss common computational methods, engineering practices, and design patterns that enable autonomy in SAR.
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Modular Reconfigurable Robotics
Jungwon Seo, Jamie Paik, and Mark YimVol. 2 (2019), pp. 63–88More LessThis article reviews the current state of the art in the development of modular reconfigurable robot (MRR) systems and suggests promising future research directions. A wide variety of MRR systems have been presented to date, and these robots promise to be versatile, robust, and low cost compared with other conventional robot systems. MRR systems thus have the potential to outperform traditional systems with a fixed morphology when carrying out tasks that require a high level of flexibility. We begin by introducing the taxonomy of MRRs based on their hardware architecture. We then examine recent progress in the hardware and the software technologies for MRRs, along with remaining technical issues. We conclude with a discussion of open challenges and future research directions.
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Control Across Scales by Positive and Negative Feedback
R. Sepulchre, G. Drion, and A. FranciVol. 2 (2019), pp. 89–113More LessFeedback is a key element of regulation, as it shapes the sensitivity of a process to its environment. Positive feedback upregulates, and negative feedback downregulates. Many regulatory processes involve a mixture of both, whether in nature or in engineering. This article revisits the mixed-feedback paradigm, with the aim of investigating control across scales. We propose that mixed feedback regulates excitability and that excitability plays a central role in multiscale neuronal signaling. We analyze this role in a multiscale network architecture inspired by neurophysiology. The nodal behavior defines a mesoscale that connects actuation at the microscale to regulation at the macroscale. We show that mixed-feedback nodal control provides regulatory principles at the network scale, with a nodal resolution. In this sense, the mixed-feedback paradigm is a control principle across scales.
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Formal Methods for Control Synthesis: An Optimization Perspective
Vol. 2 (2019), pp. 115–140More LessIn control theory, complicated dynamics such as systems of (nonlinear) differential equations are controlled mostly to achieve stability. This fundamental property, which can be with respect to a desired operating point or a prescribed trajectory, is often linked with optimality, which requires minimizing a certain cost along the trajectories of a stable system. In formal verification (model checking), simple systems, such as finite-state transition graphs that model computer programs or digital circuits, are checked against rich specifications given as formulas of temporal logics. The formal synthesis problem, in which the goal is to synthesize or control a finite system from a temporal logic specification, has recently received increased interest. In this article, we review some recent results on the connection between optimal control and formal synthesis. Specifically, we focus on the following problem: Given a cost and a correctness temporal logic specification for a dynamical system, generate an optimal control strategy that satisfies the specification. We first provide a short overview of automata-based methods, in which the dynamics of the system are mapped to a finite abstraction that is then controlled using an automaton corresponding to the specification. We then provide a detailed overview of a class of methods that rely on mapping the specification and the dynamics to constraints of an optimization problem. We discuss advantages and limitations of these two types of approaches and suggest directions for future research.
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Discrete Event Systems: Modeling, Observation, and Control
Vol. 2 (2019), pp. 141–159More LessThis article begins with an introduction to the modeling of discrete event systems, a class of dynamical systems with discrete states and event-driven dynamics. It then focuses on logical discrete event models, primarily automata, and reviews observation and control problems and their solution methodologies. Specifically, it discusses diagnosability and opacity in the context of partially observed discrete event systems. It then discusses supervisory control for both fully and partially observed systems. The emphasis is on presenting fundamental results first, followed by a discussion of current research directions.
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From Visual Understanding to Complex Object Manipulation
Vol. 2 (2019), pp. 161–179More LessPlanning and executing object manipulation requires integrating multiple sensory and motor channels while acting under uncertainty and complying with task constraints. As the modern environment is tuned for human hands, designing robotic systems with similar manipulative capabilities is crucial. Research on robotic object manipulation is divided into smaller communities interested in, e.g., motion planning, grasp planning, sensorimotor learning, and tool use. However, few attempts have been made to combine these areas into holistic systems. In this review, we aim to unify the underlying mechanics of grasping and in-hand manipulation by focusing on the temporal aspects of manipulation, including visual perception, grasp planning and execution, and goal-directed manipulation. Inspired by human manipulation, we envision that an emphasis on the temporal integration of these processes opens the way for human-like object use by robots.
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Robotic Micromanipulation: Fundamentals and Applications
Vol. 2 (2019), pp. 181–203More LessRobotic micromanipulation is a relatively young field. However, after three decades of development and evolution, the fundamental physics; techniques for sensing, actuation, and control; tool sets and systems; and, more importantly, a research community are now in place. This article reviews the fundamentals of robotic micromanipulation, including how micromanipulators and end effectors are actuated and controlled, how remote physical fields are utilized for micromanipulation, how visual servoing is implemented under an optical microscope, how force is sensed and controlled at the micro- and nanonewton levels, and the similarities and differences between robotic manipulation at the micro- and macroscales. We also review representative milestones over the past three decades and discuss potential future trends of this field.
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Microrobotics and Microorganisms: Biohybrid Autonomous Cellular Robots
Vol. 2 (2019), pp. 205–230More LessBiohybrid microrobots, composed of a living organism integrated with an artificial carrier, offer great advantages for the miniaturization of devices with onboard actuation, sensing, and control functionalities and can perform multiple tasks, including manipulation, cargo delivery, and targeting, at nano- and microscales. Over the past decade, various microorganisms and artificial carriers have been integrated to develop unique biohybrid microrobots that can swim or crawl inside the body, in order to overcome the challenges encountered by the current cargo delivery systems. Here, we first focus on the locomotion mechanisms of microorganisms at the microscale, crucial criteria for the selection of biohybrid microrobot components, and the integration of the selected artificial and biological components using various physical and chemical techniques. We then critically review biohybrid microrobots that have been designed and used to perform specific tasks in vivo. Finally, we discuss key challenges, including fabrication efficiency, swarm manipulation, in vivo imaging, and immunogenicity, that should be overcome before biohybrid microrobots transition to clinical use.
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Toward Autonomy in Sub-Gram Terrestrial Robots
Vol. 2 (2019), pp. 231–252More LessResearch toward small, autonomous, and mobile robots is inspired by both the insects we see around us and numerous applications, from inspection of jet engines and civil infrastructure to medical procedures. When comparing existing robots at small scales with their biological counterparts, the capability for autonomous operation is a glaring contrast. This review describes the state of the art in robotics at sub-gram scales along with the progress toward autonomy in power, mobility, and control at these small sizes. Metrics are described to both quantify the performance of existing sub-gram robots (e.g., speed and cost of transport) and define a more quantitative path toward autonomy (e.g., mass-specific run time and traversal probability). These metrics from existing robots are also compared with those of insects to identify significant performance gaps and highlight important areas for future study.
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A Tour of Reinforcement Learning: The View from Continuous Control
Vol. 2 (2019), pp. 253–279More LessThis article surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications. It reviews the general formulation, terminology, and typical experimental implementations of reinforcement learning as well as competing solution paradigms. In order to compare the relative merits of various techniques, it presents a case study of the linear quadratic regulator (LQR) with unknown dynamics, perhaps the simplest and best-studied problem in optimal control. It also describes how merging techniques from learning theory and control can provide nonasymptotic characterizations of LQR performance and shows that these characterizations tend to match experimental behavior. In turn, when revisiting more complex applications, many of the observed phenomena in LQR persist. In particular, theory and experiment demonstrate the role and importance of models and the cost of generality in reinforcement learning algorithms. The article concludes with a discussion of some of the challenges in designing learning systems that safely and reliably interact with complex and uncertain environments and how tools from reinforcement learning and control might be combined to approach these challenges.
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System Identification: A Machine Learning Perspective
A. Chiuso, and G. PillonettoVol. 2 (2019), pp. 281–304More LessEstimation of functions from sparse and noisy data is a central theme in machine learning. In the last few years, many algorithms have been developed that exploit Tikhonov regularization theory and reproducing kernel Hilbert spaces. These are the so-called kernel-based methods, which include powerful approaches like regularization networks, support vector machines, and Gaussian regression. Recently, these techniques have also gained popularity in the system identification community. In both linear and nonlinear settings, kernels that incorporate information on dynamic systems, such as the smoothness and stability of the input–output map, can challenge consolidated approaches based on parametric model structures. In the classical parametric setting, the complexity of the model (the model order) needs to be chosen, typically from a finite family of alternatives, by trading bias and variance. This (discrete) model order selection step may be critical, especially when the true model does not belong to the model class. In regularization-based approaches, model complexity is controlled by tuning (continuous) regularization parameters, making the model selection step more robust. In this article, we review these new kernel-based system identification approaches and discuss extensions based on nuclear and
norms.
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A Perspective on Incentive Design: Challenges and Opportunities
Vol. 2 (2019), pp. 305–338More LessThe increasingly tight coupling between humans and system operations in domains ranging from intelligent infrastructure to e-commerce has led to a challenging new class of problems founded on a well-established area of research: incentive design. There is a clear need for a new tool kit for designing mechanisms that help coordinate self-interested parties while avoiding unexpected outcomes in the face of information asymmetries, exogenous uncertainties from dynamic environments, and resource constraints. This article provides a perspective on the current state of the art in incentive design from three core communities—economics, control theory, and machine learning—and highlights interesting avenues for future research at the interface of these domains.
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Internal Models in Biological Control
Vol. 2 (2019), pp. 339–364More LessRationality principles such as optimal feedback control and Bayesian inference underpin a probabilistic framework that has accounted for a range of empirical phenomena in biological sensorimotor control. To facilitate the optimization of flexible and robust behaviors consistent with these theories, the ability to construct internal models of the motor system and environmental dynamics can be crucial. In the context of this theoretic formalism, we review the computational roles played by such internal models and the neural and behavioral evidence for their implementation in the brain.
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Agricultural Robotics
Vol. 2 (2019), pp. 365–392More LessA key goal of contemporary agriculture is to dramatically increase production of food, feed, fiber, and biofuel products in a sustainable fashion, facing the pressure of diminishing farm labor supply. Agricultural robots can accelerate plant breeding and advance data-driven precision farming with significantly reduced labor inputs by providing task-appropriate sensing and actuation at fine spatiotemporal resolutions. This article highlights the distinctive challenges imposed on ground robots by agricultural environments, which are characterized by wide variations in environmental conditions, diversity and complexity of plant canopy structures, and intraspecies biological variation of physical and chemical characteristics and responses to the environment. Existing approaches to address these challenges are presented, along with their limitations; possible future directions are also discussed. Two key observations are that biology (breeding) and horticultural practices can reduce variabilities at the source and that publicly available benchmark data sets are needed to increase perception robustness and performance despite variability.
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Modeling and Estimation for Advanced Battery Management
Vol. 2 (2019), pp. 393–426More LessThe commercialization of lithium-ion batteries enabled the widespread use of portable consumer electronics and serious efforts to electrify trans-portation. Managing the potent brew of lithium-ion batteries in the large quantities necessary for vehicle propulsion is still challenging. From space applications a billion miles from Earth to the daily commute of a hybrid electric automobile, these batteries require sophisticated battery management systems based on accurate estimation of battery internal states. This system is the brain of the battery and is responsible for estimating the state of charge, state of health, state of power, and temperature. The state estimation relies on accurate prediction of complex electrochemical, thermal, and mechanical phenomena, which increases the importance of model and parameter accuracy. Moreover, as the batteries age, how should the parameters of the model change to accurately represent the performance, and how can we leverage the limited sensor information from the measured terminal voltage and sparse surface temperatures available in a battery system? With a frugal sensor set, what is the optimal sensor placement? This article reviews estimation techniques and error bounds regarding sensor noise and modeling errors, and concludes with an outlook on the research that will be necessary to enable fast charging, repurposing of batteries for grid energy storage, degradation prediction, and fault detection.
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Cyber-Physical Manufacturing Systems
Vol. 2 (2019), pp. 427–443More LessCyber-physical systems, in which computation and networking technologies interact with physical systems, have made great strides into manufacturing systems. From the early days, when electromechanical relays were used to automate conveyors and machines, through the introduction of programmable logic controllers and computer numerical control, computing and networking have become pervasive in manufacturing systems. By increasing the amount of automation at multiple levels within a factory and across the enterprise, cyber-physical manufacturing systems enable higher productivity and higher quality as well as lower costs.
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The Engineering of Climate Engineering
Vol. 2 (2019), pp. 445–467More LessWhile reducing anthropogenic greenhouse gas emissions remains the most essential element of any strategy to manage climate change risk, it is also in principle possible to directly cool the climate by reflecting some sunlight back to space. Such climate engineering approaches include adding aerosols to the stratosphere and marine cloud brightening. Assessing whether these ideas could reduce risk requires a broad, multidisciplinary research effort spanning climate science, social sciences, and governance. However, if such strategies were ever used, the effort would also constitute one of the most critical engineering design and control challenges ever considered: making real-time decisions for a highly uncertain and nonlinear dynamic system with many input variables, many measurements, and a vast number of internal degrees of freedom, the dynamics of which span a wide range of timescales. Here, we review the engineering design aspects of climate engineering, discussing both progress to date and remaining challenges that will need to be addressed.
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