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Chapter 72 — Social Robotics

Cynthia Breazeal, Kerstin Dautenhahn and Takayuki Kanda

This chapter surveys some of the principal research trends in Social Robotics and its application to human–robot interaction (HRI). Social (or Sociable) robots are designed to interact with people in a natural, interpersonal manner – often to achieve positive outcomes in diverse applications such as education, health, quality of life, entertainment, communication, and tasks requiring collaborative teamwork. The long-term goal of creating social robots that are competent and capable partners for people is quite a challenging task. They will need to be able to communicate naturally with people using both verbal and nonverbal signals. They will need to engage us not only on a cognitive level, but on an emotional level as well in order to provide effective social and task-related support to people. They will need a wide range of socialcognitive skills and a theory of other minds to understand human behavior, and to be intuitively understood by people. A deep understanding of human intelligence and behavior across multiple dimensions (i. e., cognitive, affective, physical, social, etc.) is necessary in order to design robots that can successfully play a beneficial role in the daily lives of people. This requires a multidisciplinary approach where the design of social robot technologies and methodologies are informed by robotics, artificial intelligence, psychology, neuroscience, human factors, design, anthropology, and more.

Overview of Autom: A robotic health coach for weight management

Author  Cynthia Breazeal

Video ID : 558

This video presents an overview of Autom, a robot designed to serve as a personal coach for weight management during a longitudinal study. Fifteen robots were deployed over a period of two months and were compared to two other conditions: A computer coach with the same dialog (but no physical or social embodiment) and a paper log (standard of care). The primary question the study addressed was long-term usage and engagement as that is the most critical to keeping weight off. The hypothesis (verified by the longitudinal study) is that the physical-social embodiment makes a positive difference in people's sustained engagement, perception of their working alliance, and social support provided by the robot (than the other two interventions). People were more engaged with the robot than the other two interventions, and the emotional bond was notable in the robot modality and much less so in the other two interventions.

Chapter 69 — Physical Human-Robot Interaction

Sami Haddadin and Elizabeth Croft

Over the last two decades, the foundations for physical human–robot interaction (pHRI) have evolved from successful developments in mechatronics, control, and planning, leading toward safer lightweight robot designs and interaction control schemes that advance beyond the current capacities of existing high-payload and highprecision position-controlled industrial robots. Based on their ability to sense physical interaction, render compliant behavior along the robot structure, plan motions that respect human preferences, and generate interaction plans for collaboration and coaction with humans, these novel robots have opened up novel and unforeseen application domains, and have advanced the field of human safety in robotics.

This chapter gives an overview on the state of the art in pHRI as of the date of publication. First, the advances in human safety are outlined, addressing topics in human injury analysis in robotics and safety standards for pHRI. Then, the foundations of human-friendly robot design, including the development of lightweight and intrinsically flexible force/torque-controlled machines together with the required perception abilities for interaction are introduced. Subsequently, motionplanning techniques for human environments, including the domains of biomechanically safe, risk-metric-based, human-aware planning are covered. Finally, the rather recent problem of interaction planning is summarized, including the issues of collaborative action planning, the definition of the interaction planning problem, and an introduction to robot reflexes and reactive control architecture for pHRI.

Safe physical human-robot collaboration

Author  Fabrizio Flacco, Alessandro De Luca

Video ID : 609

The video summarizes the state of the on-going research activities on physical human-robot collaboration (pHRC) at the DIAG Robotics Lab, Sapienza University of Rome, as of March 2013, and performed within the European Research Project FP7 287511 SAPHARI (http://www.saphari.eu) Reference: F. Flacco, A. De Luca: Safe physical human-robot collaboration, IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Tokyo (2013)

Chapter 21 — Actuators for Soft Robotics

Alin Albu-Schäffer and Antonio Bicchi

Although we do not know as yet how robots of the future will look like exactly, most of us are sure that they will not resemble the heavy, bulky, rigid machines dangerously moving around in old fashioned industrial automation. There is a growing consensus, in the research community as well as in expectations from the public, that robots of the next generation will be physically compliant and adaptable machines, closely interacting with humans and moving safely, smoothly and efficiently - in other terms, robots will be soft.

This chapter discusses the design, modeling and control of actuators for the new generation of soft robots, which can replace conventional actuators in applications where rigidity is not the first and foremost concern in performance. The chapter focuses on the technology, modeling, and control of lumped parameters of soft robotics, that is, systems of discrete, interconnected, and compliant elements. Distributed parameters, snakelike and continuum soft robotics, are presented in Chap. 20, while Chap. 23 discusses in detail the biomimetic motivations that are often behind soft robotics.

Throwing a ball with the DLR VS-Joint

Author  Sebastian Wolf, Gerd Hirzinger

Video ID : 549

The video shows the difference between a stiff and a flexible actuator in a 1-DOF throwing demonstration. The variable stiffness actuator (VS-joint) can store potential energy in a strike out movement and release it by accelerating the lever and ball. Additional energy is transferred to the lever by stiffening up during the forward motion.

Chapter 40 — Mobility and Manipulation

Oliver Brock, Jaeheung Park and Marc Toussaint

Mobile manipulation requires the integration of methodologies from all aspects of robotics. Instead of tackling each aspect in isolation,mobilemanipulation research exploits their interdependence to solve challenging problems. As a result, novel views of long-standing problems emerge. In this chapter, we present these emerging views in the areas of grasping, control, motion generation, learning, and perception. All of these areas must address the shared challenges of high-dimensionality, uncertainty, and task variability. The section on grasping and manipulation describes a trend towards actively leveraging contact and physical and dynamic interactions between hand, object, and environment. Research in control addresses the challenges of appropriately coupling mobility and manipulation. The field of motion generation increasingly blurs the boundaries between control and planning, leading to task-consistent motion in high-dimensional configuration spaces, even in dynamic and partially unknown environments. A key challenge of learning formobilemanipulation consists of identifying the appropriate priors, and we survey recent learning approaches to perception, grasping, motion, and manipulation. Finally, a discussion of promising methods in perception shows how concepts and methods from navigation and active perception are applied.

Adaptive synergies for a humanoid robot hand

Author  Centro di Ricerca Enrico Piaggio

Video ID : 658

We present the first implementation of the UNIPI-hand, a highly integrated prototype of an anthropomorphic hand that conciliates the idea of adaptive synergies with a human-form factor. The video validates the hand's versatility by showing grasp and manipulation actions on a variety of objects.

Chapter 72 — Social Robotics

Cynthia Breazeal, Kerstin Dautenhahn and Takayuki Kanda

This chapter surveys some of the principal research trends in Social Robotics and its application to human–robot interaction (HRI). Social (or Sociable) robots are designed to interact with people in a natural, interpersonal manner – often to achieve positive outcomes in diverse applications such as education, health, quality of life, entertainment, communication, and tasks requiring collaborative teamwork. The long-term goal of creating social robots that are competent and capable partners for people is quite a challenging task. They will need to be able to communicate naturally with people using both verbal and nonverbal signals. They will need to engage us not only on a cognitive level, but on an emotional level as well in order to provide effective social and task-related support to people. They will need a wide range of socialcognitive skills and a theory of other minds to understand human behavior, and to be intuitively understood by people. A deep understanding of human intelligence and behavior across multiple dimensions (i. e., cognitive, affective, physical, social, etc.) is necessary in order to design robots that can successfully play a beneficial role in the daily lives of people. This requires a multidisciplinary approach where the design of social robot technologies and methodologies are informed by robotics, artificial intelligence, psychology, neuroscience, human factors, design, anthropology, and more.

A robot that forms a good spatial formation

Author  Takayuki Kanda

Video ID : 257

The video illustrates one of capabilities of social robots developed for making interaction with people smooth and natural. With the developed technique, the robot has the capability to detect the attention of the user based on his location and to adjust its standing position so that it forms a good spatial formation, in which they can easily talk about the object of their attention. In the video, when the user looks around for the computers in a room, the robot moves to a location where it is convenient to explain the computers.

Chapter 8 — Motion Control

Wan Kyun Chung, Li-Chen Fu and Torsten Kröger

This chapter will focus on the motion control of robotic rigid manipulators. In other words, this chapter does not treat themotion control ofmobile robots, flexible manipulators, and manipulators with elastic joints. The main challenge in the motion control problem of rigid manipulators is the complexity of their dynamics and uncertainties. The former results from nonlinearity and coupling in the robot manipulators. The latter is twofold: structured and unstructured. Structured uncertainty means imprecise knowledge of the dynamic parameters and will be touched upon in this chapter, whereas unstructured uncertainty results from joint and link flexibility, actuator dynamics, friction, sensor noise, and unknown environment dynamics, and will be treated in other chapters. In this chapter, we begin with an introduction to motion control of robot manipulators from a fundamental viewpoint, followed by a survey and brief review of the relevant advanced materials. Specifically, the dynamic model and useful properties of robot manipulators are recalled in Sect. 8.1. The joint and operational space control approaches, two different viewpoints on control of robot manipulators, are compared in Sect. 8.2. Independent joint control and proportional– integral–derivative (PID) control, widely adopted in the field of industrial robots, are presented in Sects. 8.3 and 8.4, respectively. Tracking control, based on feedback linearization, is introduced in Sect. 8.5. The computed-torque control and its variants are described in Sect. 8.6. Adaptive control is introduced in Sect. 8.7 to solve the problem of structural uncertainty, whereas the optimality and robustness issues are covered in Sect. 8.8. To compute suitable set point signals as input values for these motion controllers, Sect. 8.9 introduces reference trajectory planning concepts. Since most controllers of robotmanipulators are implemented by using microprocessors, the issues of digital implementation are discussed in Sect. 8.10. Finally, learning control, one popular approach to intelligent control, is illustrated in Sect. 8.11.

JediBot - Experiments in human-robot sword-fighting

Author  Torsten Kröger, Ken Oslund, Tim Jenkins, Dan Torczynski, Nicholas Hippenmeyer, Radu Bogdan Rusu, Oussama Khatib

Video ID : 759

Real-world sword-fighting between human opponents requires extreme agility, fast reaction time and dynamic perception capabilities. This video shows experimental results achieved with a 3-D vision system and a highly reactive control architecture which allowfs a robot to sword fight against human opponents. An online trajectory generator is used as an intermediate layer between low-level trajectory-following controllers and high-level visual perception. This architecture enables robots to react nearly instantaneously to the unpredictable human motions perceived by the vision system as well as to sudden sword contacts detected by force and torque sensors. Results show how smooth and highly dynamic motions are generated on-the-fly while using the vision and force/torque sensor signals in the feedback loops of the robot-motion controller. Reference: T. Kröger, K. Oslund, T. Jenkins, D. Torczynski, N. Hippenmeyer, R. B. Rusu, O. Khatib: JediBot - Experiments in human-robot sword-fighting, Proc. Int. Symp. Exp. Robot., Québec City (2012)

Virtual whiskers - Highly responsive robot collision avoidance

Author  Thomas Schlegl, Torsten Kröger, Andre Gaschler, Oussama Khatib, Hubert Zangl

Video ID : 758

All mammals but humans use whiskers in order to rapidly acquire information about objects in the vicinity of the head. Collisions of the head and objects can be avoided as the contact point is moved from the body surface to the whiskers. Such a behavior is also highly desirable during many robot tasks such as for human-robot interaction. This video shows the use of novel capacitive proximity sensors so that robots can sense when they approach a human (or an object) and react before they actually collide with it. The sensors are flexible and thin so that they feature skin-like properties and can be attached to various robotic links and joint shapes. In comparison to capacitive proximity sensors, the proposed virtual whiskers offer better sensitivity towards small conductive as well as non-conductive objects. Equipped with the new proximity sensors, a seven-joint robot for human-robot interaction tasks demonstrates the efficiency and responsiveness in this video. Reference: T. Schlegl, T. Kröger, A. Gaschler, O. Khatib, H. Zangl: Virtual whiskers - Highly responsive robot collision avoidance, Proc. IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Tokyo (2013)

Chapter 25 — Underwater Robots

Hyun-Taek Choi and Junku Yuh

Covering about two-thirds of the earth, the ocean is an enormous system that dominates processes on the Earth and has abundant living and nonliving resources, such as fish and subsea gas and oil. Therefore, it has a great effect on our lives on land, and the importance of the ocean for the future existence of all human beings cannot be overemphasized. However, we have not been able to explore the full depths of the ocean and do not fully understand the complex processes of the ocean. Having said that, underwater robots including remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) have received much attention since they can be an effective tool to explore the ocean and efficiently utilize the ocean resources. This chapter focuses on design issues of underwater robots including major subsystems such as mechanical systems, power sources, actuators and sensors, computers and communications, software architecture, and manipulators while Chap. 51 covers modeling and control of underwater robots.

Preliminary results of sonar-based SLAM using landmarks

Author  Hyun-Taek Choi

Video ID : 794

This video records preliminary experimental results of a sonar-based SLAM algorithm developed by KRISO (Korea Research Institute of Ships and Ocean Engineering). A position obtained by the proposed probability-based landmark-recognition method and landmarks especially designed for sonar is used to correct the position estimated by IMU/DVL navigation using EKF (extended Kalman filter).

Chapter 58 — Robotics in Hazardous Applications

James Trevelyan, William R. Hamel and Sung-Chul Kang

Robotics researchers have worked hard to realize a long-awaited vision: machines that can eliminate the need for people to work in hazardous environments. Chapter 60 is framed by the vision of disaster response: search and rescue robots carrying people from burning buildings or tunneling through collapsed rock falls to reach trapped miners. In this chapter we review tangible progress towards robots that perform routine work in places too dangerous for humans. Researchers still have many challenges ahead of them but there has been remarkable progress in some areas. Hazardous environments present special challenges for the accomplishment of desired tasks depending on the nature and magnitude of the hazards. Hazards may be present in the form of radiation, toxic contamination, falling objects or potential explosions. Technology that specialized engineering companies can develop and sell without active help from researchers marks the frontier of commercial feasibility. Just inside this border lie teleoperated robots for explosive ordnance disposal (EOD) and for underwater engineering work. Even with the typical tenfold disadvantage in manipulation performance imposed by the limits of today’s telepresence and teleoperation technology, in terms of human dexterity and speed, robots often can offer a more cost-effective solution. However, most routine applications in hazardous environments still lie far beyond the feasibility frontier. Fire fighting, remediating nuclear contamination, reactor decommissioning, tunneling, underwater engineering, underground mining and clearance of landmines and unexploded ordnance still present many unsolved problems.

NanoMag magnetic crawler for remote inspection

Author  James P. Trevelyan

Video ID : 591

Promotional video for an inspection robot. This robot is clearly limited to working on magnetic surfaces.

Chapter 6 — Model Identification

John Hollerbach, Wisama Khalil and Maxime Gautier

This chapter discusses how to determine the kinematic parameters and the inertial parameters of robot manipulators. Both instances of model identification are cast into a common framework of least-squares parameter estimation, and are shown to have common numerical issues relating to the identifiability of parameters, adequacy of the measurement sets, and numerical robustness. These discussions are generic to any parameter estimation problem, and can be applied in other contexts.

For kinematic calibration, the main aim is to identify the geometric Denavit–Hartenberg (DH) parameters, although joint-based parameters relating to the sensing and transmission elements can also be identified. Endpoint sensing or endpoint constraints can provide equivalent calibration equations. By casting all calibration methods as closed-loop calibration, the calibration index categorizes methods in terms of how many equations per pose are generated.

Inertial parameters may be estimated through the execution of a trajectory while sensing one or more components of force/torque at a joint. Load estimation of a handheld object is simplest because of full mobility and full wrist force-torque sensing. For link inertial parameter estimation, restricted mobility of links nearer the base as well as sensing only the joint torque means that not all inertial parameters can be identified. Those that can be identified are those that affect joint torque, although they may appear in complicated linear combinations.

Dynamic identification of a parallel robot: Trajectory with load

Author  Maxime Gautier

Video ID : 485

This video shows a trajectory with a known mass payload attached to the platform, used to identify the dynamic parameters and joint drive gains of a parallel prototype robot Orthoglyde. Details and results are given in the paper: S. Briot, M. Gautier: Global identification of joint drive gains and dynamic parameters of parallel robots, Multibody Syst. Dyn. 33(1), 3-26 (2015); doi 10.1007/s11044-013-9403-6