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- Volume 3, 2020
Annual Review of Control, Robotics, and Autonomous Systems - Volume 3, 2020
Volume 3, 2020
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Robotic Self-Replication
Vol. 3 (2020), pp. 1–24More LessThe concept of an artificial corporeal machine that can reproduce has attracted the attention of researchers from various fields over the past century. Some have approached the topic with a desire to understand biological life and develop artificial versions; others have examined it as a potentially practical way to use material resources from the moon and Mars to bootstrap the exploration and colonization of the solar system. This review considers both bodies of literature, with an emphasis on the underlying principles required to make self-replicating robotic systems from raw materials a reality. We then illustrate these principles with machines from our laboratory and others and discuss how advances in new manufacturing processes such as 3-D printing can have a synergistic effect in advancing the development of such systems.
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Robots That Use Language
Vol. 3 (2020), pp. 25–55More LessThis article surveys the use of natural language in robotics from a robotics point of view. To use human language, robots must map words to aspects of the physical world, mediated by the robot's sensors and actuators. This problem differs from other natural language processing domains due to the need to ground the language to noisy percepts and physical actions. Here, we describe central aspects of language use by robots, including understanding natural language requests, using language to drive learning about the physical world, and engaging in collaborative dialogue with a human partner. We describe common approaches, roughly divided into learning methods, logic-based methods, and methods that focus on questions of human–robot interaction. Finally, we describe several application domains for language-using robots.
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Magnetic Methods in Robotics
Vol. 3 (2020), pp. 57–90More LessThe goal of this article is to provide a thorough introduction to the state of the art in magnetic methods for remote-manipulation and wireless-actuation tasks in robotics. The article synthesizes prior works using a unified notation, enabling straightforward application in robotics. It begins with a discussion of the magnetic fields generated by magnetic materials and electromagnets, how magnetic materials become magnetized in an applied field, and the forces and torques generated on magnetic objects. It then describes systems used to generate and control applied magnetic fields, including both electromagnetic and permanent-magnet systems. Finally, it surveys work from a variety of robotic application areas in which researchers have utilized magnetic methods, including microrobotics, medical robotics, haptics, and aerospace.
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Mobile Sensor Networks and Control: Adaptive Sampling of Spatiotemporal Processes
Vol. 3 (2020), pp. 91–114More LessThe control of mobile sensor networks uses sensor measurements to update a model of an unknown or estimated process, which in turn guides the collection of subsequent measurements—a feedback control framework called adaptive sampling. Applications for adaptive sampling exist in a wide range of settings, especially for unmanned or autonomous vehicles that can be deployed cheaply and in cooperative groups. The dynamics of mobile sensor platforms are often simplified to planar self-propelled particles subject to the ambient flow of the surrounding fluid. Sensor measurements are assimilated into continuous or discrete models of the process of interest, which in general can vary in space and time. The variability of the estimated process is one metric to score future candidate sampling trajectories, along with information- and uncertainty-based metrics. Sampling tasks are allocated to the network using centralized or decentralized optimization, in order to avoid redundant measurements and observational gaps.
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Network Effects on the Robustness of Dynamic Systems
Vol. 3 (2020), pp. 115–149More LessWe review selected results related to the robustness of networked systems in finite and asymptotically large size regimes in static and dynamical settings. In the static setting, within the framework of flow over finite networks, we discuss the effect of physical constraints on robustness to loss in link capacities. In the dynamical setting, we review several settings in which small-gain-type analysis provides tight robustness guarantees for linear dynamics over finite networks toward worst-case and stochastic disturbances. We discuss network flow dynamic settings where nonlinear techniques facilitate understanding the effect, on robustness, of constraints on capacity and information, substituting information with control action, and cascading failure. We also contrast cascading failure with a representative contagion model. For asymptotically large networks, we discuss the role of network properties in connecting microscopic shocks to emergent macroscopic fluctuations under linear dynamics as well as for economic networks at equilibrium. Through this review, we aim to achieve two objectives: to highlight selected settings in which the role of the interconnectivity structure of a network in its robustness is well understood, and to highlight a few additional settings in which existing system-theoretic tools give tight robustness guarantees and that are also appropriate avenues for future network-theoretic investigations.
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Routing on Traffic Networks Incorporating Past Memory up to Real-Time Information on the Network State
Vol. 3 (2020), pp. 151–172More LessIn this review, we discuss routing algorithms for the dynamic traffic assignment (DTA) problem that assigns traffic flow in a given road network as realistically as possible. We present a new class of so-called routing operators that route traffic flow at intersections based on either real-time information about the status of the network or historical data. These routing operators thus cover the distribution of traffic flow at all possible intersections. To model traffic flow on the links, we use a well-known macroscopic ordinary delay differential equation. We prove the existence and uniqueness of the solutions of the resulting DTA for a broad class of routing operators. This new routing approach is required and justified by the increased usage of real-time information on the network provided by map services, changing the laws of routing significantly. Because these map and routing services have a huge impact on the infrastructure of cities, a more precise mathematical description of the emerging new traffic patterns and effects becomes crucial for understanding and improving road and city conditions.
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Amphibious and Sprawling Locomotion: From Biology to Robotics and Back
Vol. 3 (2020), pp. 173–193More LessA milestone in vertebrate evolution, the transition from water to land, owes its success to the development of a sprawling body plan that enabled an amphibious lifestyle. The body, originally adapted for swimming, evolved to benefit from limbs that enhanced its locomotion capabilities on submerged and dry ground. The first terrestrial animals used sprawling locomotion, a type of legged locomotion in which limbs extend laterally from the body (as opposed to erect locomotion, in which limbs extend vertically below the body). This type of locomotion—exhibited, for instance, by salamanders, lizards, and crocodiles—has been studied in a variety of fields, including neuroscience, biomechanics, evolution, and paleontology. Robotics can benefit from these studies to design amphibious robots capable of swimming and walking, with interesting applications in field robotics, in particular for search and rescue, inspection, and environmental monitoring. In return, robotics can provide useful scientific tools to test hypotheses in neuroscience, biomechanics, and paleontology. For instance, robots have been used to test hypotheses about the organization of neural circuits that can switch between swimming and walking under the control of simple modulation signals, as well as to identify the most likely gaits of extinct sprawling animals. Here, I review different aspects of amphibious and sprawling locomotion, namely gait characteristics, neurobiology, numerical models, and sprawling robots, and discuss fruitful interactions between robotics and other scientific fields.
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Stochastic Dynamical Modeling of Turbulent Flows
Vol. 3 (2020), pp. 195–219More LessAdvanced measurement techniques and high-performance computing have made large data sets available for a range of turbulent flows in engineering applications. Drawing on this abundance of data, dynamical models that reproduce structural and statistical features of turbulent flows enable effective model-based flow control strategies. This review describes a framework for completing second-order statistics of turbulent flows using models based on the Navier–Stokes equations linearized around the turbulent mean velocity. Dynamical couplings between states of the linearized model dictate structural constraints on the statistics of flow fluctuations. Colored-in-time stochastic forcing that drives the linearized model is then sought to account for and reconcile dynamics with available data (that is, partially known statistics). The number of dynamical degrees of freedom that are directly affected by stochastic excitation is minimized as a measure of model parsimony. The spectral content of the resulting colored-in-time stochastic contribution can alternatively arise from a low-rank structural perturbation of the linearized dynamical generator, pointing to suitable dynamical corrections that may account for the absence of the nonlinear interactions in the linearized model.
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Robotics In Vivo: A Perspective on Human–Robot Interaction in Surgical Robotics
Vol. 3 (2020), pp. 221–242More LessThis article reviews recent work on surgical robots that have been used or tested in vivo, focusing on aspects related to human–robot interaction. We present the general design requirements that should be considered when developing such robots, including the clinical requirements and the technologies needed to satisfy them. We also discuss the human aspects related to the design of these robots, considering the challenges facing surgeons when using robots in the operating room, and the safety issues of such systems. We then survey recent work in seven different surgical settings: urology and gynecology, orthopedic surgery, cardiac surgery, head and neck surgery, neurosurgery, radiotherapy, and bronchoscopy. We conclude with the open problems and recommendations on how to move forward in this research area.
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The Synergy Between Neuroscience and Control Theory: The Nervous System as Inspiration for Hard Control Challenges
Vol. 3 (2020), pp. 243–267More LessHere, we review the role of control theory in modeling neural control systems through a top-down analysis approach. Specifically, we examine the role of the brain and central nervous system as the controller in the organism, connected to but isolated from the rest of the animal through insulated interfaces. Though biological and engineering control systems operate on similar principles, they differ in several critical features, which makes drawing inspiration from biology for engineering controllers challenging but worthwhile. We also outline a procedure that the control theorist can use to draw inspiration from the biological controller: starting from the intact, behaving animal; designing experiments to deconstruct and model hierarchies of feedback; modifying feedback topologies; perturbing inputs and plant dynamics; using the resultant outputs to perform system identification; and tuning and validating the resultant control-theoretic model using specially engineered robophysical models.
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Learning-Based Model Predictive Control: Toward Safe Learning in Control
Vol. 3 (2020), pp. 269–296More LessRecent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. Model predictive control (MPC), as the prime methodology for constrained control, offers a significant opportunity to exploit the abundance of data in a reliable manner, particularly while taking safety constraints into account. This review aims at summarizing and categorizing previous research on learning-based MPC, i.e., the integration or combination of MPC with learning methods, for which we consider three main categories. Most of the research addresses learning for automatic improvement of the prediction model from recorded data. There is, however, also an increasing interest in techniques to infer the parameterization of the MPC controller, i.e., the cost and constraints, that lead to the best closed-loop performance. Finally, we discuss concepts that leverage MPC to augment learning-based controllers with constraint satisfaction properties.
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Recent Advances in Robot Learning from Demonstration
Vol. 3 (2020), pp. 297–330More LessIn the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. The choice of LfD over other robot learning methods is compelling when ideal behavior can be neither easily scripted (as is done in traditional robot programming) nor easily defined as an optimization problem, but can be demonstrated. While there have been multiple surveys of this field in the past, there is a need for a new one given the considerable growth in the number of publications in recent years. This review aims to provide an overview of the collection of machine-learning methods used to enable a robot to learn from and imitate a teacher. We focus on recent advancements in the field and present an updated taxonomy and characterization of existing methods. We also discuss mature and emerging application areas for LfD and highlight the significant challenges that remain to be overcome both in theory and in practice.
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Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics
Vol. 3 (2020), pp. 331–360More LessHistorically, scalability has been a major challenge for the successful application of semidefinite programming in fields such as machine learning, control, and robotics. In this article, we survey recent approaches to this challenge, including those that exploit structure (e.g., sparsity and symmetry) in a problem, those that produce low-rank approximate solutions to semidefinite programs, those that use more scalable algorithms that rely on augmented Lagrangian techniques and the alternating-direction method of multipliers, and those that trade off scalability with conservatism (e.g., by approximating semidefinite programs with linear and second-order cone programs). For each class of approaches, we provide a high-level exposition, an entry point to the corresponding literature, and examples drawn from machine learning, control, or robotics. We also present a list of software packages that implement many of the techniques discussed in the review. Our hope is that this article will serve as a gateway to the rich and exciting literature on scalable semidefinite programming for both theorists and practitioners.
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The Inerter: A Retrospective
Vol. 3 (2020), pp. 361–391More LessThis article provides an introduction and overview of the inerter concept and device. Careful attention is given to the distinction between the inerter as an ideal modeling element and devices that approximate the ideal behavior. The background is given to the formal definition of the inerter as a mechanical one-port with terminal forces proportional to the relative acceleration between them. Four major methods of construction are described and modeled. The discussion focuses particularly on the notion of terminals, the distinction between a device and an effect, sign reversals, back driving in geared systems, the conceptual aspects of the modeling step for inerter embodiments, and the problem of reverse engineering to discover a purpose. The article includes an analysis and discussion of the rotational inerter, a brief review of the ideas of passive network synthesis that led to the inerter concept, and an analysis and discussion of several examples of integrated mechanical devices. It concludes with an imaginary dialogue between the author and an interlocutor on the understanding and purpose of the inerter.
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Port-Hamiltonian Modeling for Control
Vol. 3 (2020), pp. 393–416More LessThis article provides a concise summary of the basic ideas and concepts in port-Hamiltonian systems theory and its use in analysis and control of complex multiphysics systems. It gives special attention to new and unexplored research directions and relations with other mathematical frameworks. Emergent control paradigms and open problems are indicated, including the relation with thermodynamics and the question of uniting the energy-processing view of control, as emphasized by port-Hamiltonian systems theory, with a complementary information-processing viewpoint.
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Automated Planning for Robotics
Vol. 3 (2020), pp. 417–439More LessModern robots are increasingly capable of performing “basic” activities such as localization, navigation, and motion planning. However, for a robot to be considered intelligent, we would like it to be able to automatically combine these capabilities in order to achieve a high-level goal. The field of automated planning (sometimes called AI planning) deals with automatically synthesizing plans that combine basic actions to achieve a high-level goal. In this article, we focus on the intersection of automated planning and robotics and discuss some of the challenges and tools available to employ automated planning in controlling robots. We review different types of planning formalisms and discuss their advantages and limitations, especially in the context of planning robot actions. We conclude with a brief guide aimed at helping roboticists choose the right planning model to endow a robot with planning capabilities.
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Scientific and Technological Challenges in RoboCup
Vol. 3 (2020), pp. 441–471More LessSince its inception in 1997, RoboCup has developed into a truly unique and long-standing research community advancing robotics and artificial intelligence through various challenges, benchmarks, and test fields. The main purposes of this article are to evaluate the research and development achievements so far and to identify new challenges and related new research issues. Unlike other robot competitions and research conferences, RoboCup eliminates the boundaries between pure research activities and the development of full system designs with hardware and software implementations at a site open to the public. It also creates specific scientific and technological research and development challenges to be addressed. In this article, we provide an overview of RoboCup, including its league structure and related research issues. We also review recent studies across several research categories to show how participants (called RoboCuppers) address the research and development challenges before, during, and after the annual competitions. Among the diversity of research issues, we highlight two unique aspects of the challenges: the platform design of the robots and the game evaluations. Both of these aspects contribute to solving the research and development challenges of RoboCup and verifying the results from a common perspective (i.e., a more objective view). Finally, we provide concluding remarks and discuss future research directions.
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