Annual Review of Control, Robotics, and Autonomous Systems

Medical robotics is a rapidly advancing discipline that is leading the evolution of robot-assisted surgery, personalized rehabilitation and assistance, and hospital automation. In China, both research and commercial developments in medical robotics have undergone exponential growth in recent years. In this review, we first give an overview of the clinical and social demands that motivate the rapid development in medical robotics. For each subdiscipline (surgery, rehabilitation and personal assistance, and hospital automation), we then summarize the major research projects sponsored by National Key Research and Development Programs. The remaining technical, commercial, and regulatory challenges are highlighted. This review also outlines some of the new opportunities in endoluminal and interventional robotics, micro- and nanorobotics, soft exoskeletons, intelligent human–robot interaction, and telemedicine and telesurgery, which may support the general uptake of robotics in medicine.

Yao Guo

Weidong Chen

Jie Zhao

Guang-Zhong Yang

Annual Review of Control Robotics and Autonomous Systems

Annu. Rev. Control Robot. Auton. Syst.

Medical Robotics: Opportunities in China

医疗机器人：中国的机遇

Recent 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.

Lukas Hewing

Kim P. Wabersich

Marcel Menner

Melanie N. Zeilinger

Learning-Based Model Predictive Control: Toward Safe Learning in Control

基于学习的模型预测控制：迈向控制中的安全学习

In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning, and decision-making for autonomous vehicles have led to great improvements in functional capabilities, with several prototypes already driving on our roads and streets. Yet challenges remain regarding guaranteed performance and safety under all driving circumstances. For instance, planning methods that provide safe and system-compliant performance in complex, cluttered environments while modeling the uncertain interaction with other traffic participants are required. Furthermore, new paradigms, such as interactive planning and end-to-end learning, open up questions regarding safety and reliability that need to be addressed. In this survey, we emphasize recent approaches for integrated perception and planning and for behavior-aware planning, many of which rely on machine learning. This raises the question of verification and safety, which we also touch upon. Finally, we discuss the state of the art and remaining challenges for managing fleets of autonomous vehicles.

Wilko Schwarting

Javier Alonso-Mora

Daniela Rus

Planning and Decision-Making for Autonomous Vehicles

自动驾驶车辆的规划与决策

This 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.

Malcolm C. Smith

The Inerter: A Retrospective

惯性元件：回顾

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.

Sumitra Ganesh

Leo Ardon

Daniel Borrajo

Deepeka Garg

Udari Madhushani Sehwag

Annapoorani Lakshmi Narayanan

Giuseppe Canonaco

Manuela M. Veloso

Generative AI Agents for Knowledge Work Augmentation in Finance

金融领域知识工作增强的生成式AI代理

This article is an attempt to reexamine the evolution of robotics research at the University of Pennsylvania and all that it entailed, covering the successes and struggles of PhD students, postdoctoral researchers, and a few dedicated faculty between 1972 and 2000. In 1945, Penn's Moore School of Electrical Engineering was famous for developing the first electronic digital computer, ENIAC, but after a few years, the research had diminished. In 1972, a new Department of Computer and Information Science was formed. I came to Penn from Stanford University's Artificial Intelligence Laboratory full of energy, enthusiasm, and the goal of establishing a similar lab at Penn. This article demonstrates the creativity and ingenuity of young professionals at the General Robotics, Automation, Sensing, and Perception (GRASP) Laboratory. We built hardware. We built software. We collaborated with psychologists and electrical and mechanical engineers and tried to build a community of roboticists. Our curiosity led us to build new vision and tactile systems and to investigate cooperative robotics systems on the ground and in the air. We used computational models supported and verified by experiments. Today, I am proud to say that almost all of the students who cycled through the GRASP Lab are successful in academia or industry.

Ruzena Bajcsy

Robotics at the University of Pennsylvania from Birth to Maturity: A Review of 30 Years of Research

宾夕法尼亚大学机器人技术从诞生到成熟：30年研究回顾

The realm of textiles spans clothing, households, healthcare, sports, and industrial applications. The deformable nature of these objects poses unique challenges that prior work on rigid objects cannot fully address. The increasing interest within the community in textile perception and manipulation has led to new methods that aim to address challenges in modeling, perception, and control, resulting in significant progress. However, this progress is often tailored to one specific textile or a subcategory of these textiles. To understand what restricts these methods and hinders current approaches from generalizing to a broader range of real-world textiles, this review provides an overview of the field, focusing specifically on how and to what extent textile variations are addressed in modeling, perception, benchmarking, and manipulation of textiles. We conclude by identifying key open problems and outlining grand challenges that will drive future advancements in the field.

Alberta Longhini

Yufei Wang

Irene Garcia-Camacho

David Blanco-Mulero

Marco Moletta

Michael Welle

Guillem Alenyà

Hang Yin

Zackory Erickson

David Held

Júlia Borràs

Danica Kragic

Unfolding the Literature: A Review of Robotic Cloth Manipulation

展开文献：机器人织物操作综述

Research at the intersection of robots and dance promises to create vehicles for expression that enable new creative pursuits and allow robots to function better, especially in human-facing scenarios. Moving this research beyond fringe spectacle and establishing it as a serious, systematic field—a proper subdiscipline of both robotics and dance—will require answering a key question: How does dance advance the fundamentals of robotics, and vice versa? Focusing on the former, this article offers glimpses of this new field with examples of meaningful contributions to control, robotics, and autonomous systems, such as novel actuator designs, improved sensing systems, salient motion profiles for robots, reproducible experiment designs, and new theories of motion derived from the study of dance. It also poses two grand challenges for the emerging field of choreobotics: developing a robust symbolic system for representing bodily action and establishing rich, repeatable testing environments for human–robot interaction.

Amy LaViers

Robots and Dance: A Promising Young Alchemy

机器人与舞蹈：一门前景广阔的年轻炼金术

Abstract Linear integrators are well known for their ability to counter static forces and improve low-frequency disturbance rejection properties in control systems. However, linear integrators introduce phase lag, which is a frequency-dependent time shift or delay. Since the early introduction of the Clegg integrator, nonlinear integrators have held the promise of providing phase advantages over linear integrators when evaluated from the perspective of a describing function. This could potentially reduce delay and therefore provide the means to surpass linear design limitations; for example, overshoot and settling times can be reduced or even avoided. In addition, loop gains can be increased, which improves the low-frequency disturbance rejection properties. For five nonlinear integrators—the Clegg integrator, a generalized first-order reset integrator called the constant-in-gain–lead-in-phase (CgLp) element, a hybrid integrator–gain system, a variable gain integrator, and a split-path integrator—this article provides a comparison and overview of recent developments in stage motion control. Benchmark examples are taken from the industrial practice of wafer scanners, which form the pivotal machines used in the manufacturing of computer chips.

Abbreviation	Annu. Rev. Control Robot. Auton. Syst.
Journal Impact	11.39
Quartiles(Global)	ROBOTICS(Q1)

ISSN	2573-5144
h-index	36

Publication Information	Publisher: Annual Reviews Inc.，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2018，Proportion of original research papers: 0.00%，Self Citation Rate:0.00%， Gold OA Rate: 30.99%

Annual Review of Control, Robotics, and Autonomous Systems

Journal Citation Format

A journal article with 1 author

A journal article with 2 authors

A journal article with 3 authors

A journal article with 5 or more authors

Books Citation Format

Thesis Citation Format

Web sites Citation Format

Patent Citation Format

Staying up late manually editing references? ivySCI automatically matches journals and helps you generate references with a single click.

Share Submission Experience

Medical Robotics: Opportunities in China

Learning-Based Model Predictive Control: Toward Safe Learning in Control

Planning and Decision-Making for Autonomous Vehicles

The Inerter: A Retrospective

Generative AI Agents for Knowledge Work Augmentation in Finance

Generative AI Agents for Knowledge Work Augmentation in Finance

Robotics at the University of Pennsylvania from Birth to Maturity: A Review of 30 Years of Research

Unfolding the Literature: A Review of Robotic Cloth Manipulation

Robots and Dance: A Promising Young Alchemy

Stage Motion Control: Nonlinear Integrators Revisited