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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.

Mobile robot helper - Mr. Helper

Author   Kazuhiro Kosuge, Manabu Sato, Norihide Kazamura

Video ID : 606

In this video, a mobile robot helper referred to as Mr. Helper is proposed. Mr. Helper consists of two 7-DOF manipulators and an omni-directional mobile base. The omnidirectional mobile base is the VUTON mechanism. In this system, a human and Mr. Helper communicate with each other by intentional force. That is, a human manipulates an object by applying intentional force/torque to the object. We design an impedance controller for each manipulator, so that the object manipulated by both arms has a specified impedance around a specified compliance center. Refrence: ICRA 2000 Video Abstracts.

Chapter 20 — Snake-Like and Continuum Robots

Ian D. Walker, Howie Choset and Gregory S. Chirikjian

This chapter provides an overview of the state of the art of snake-like (backbones comprised of many small links) and continuum (continuous backbone) robots. The history of each of these classes of robot is reviewed, focusing on key hardware developments. A review of the existing theory and algorithms for kinematics for both types of robot is presented, followed by a summary ofmodeling of locomotion for snake-like and continuum mechanisms.

CMU medical snake robot

Author  Howie Choset

Video ID : 175

Video of CMU medical snake robot performing a closed-chest ablation of left atrial appendage.

Chapter 76 — Evolutionary Robotics

Stefano Nolfi, Josh Bongard, Phil Husbands and Dario Floreano

Evolutionary Robotics is a method for automatically generating artificial brains and morphologies of autonomous robots. This approach is useful both for investigating the design space of robotic applications and for testing scientific hypotheses of biological mechanisms and processes. In this chapter we provide an overview of methods and results of Evolutionary Robotics with robots of different shapes, dimensions, and operation features. We consider both simulated and physical robots with special consideration to the transfer between the two worlds.

A swarm-bot of eight robots displaying coordinated motion

Author  Stefano Nolfi, Gianluca Baldassarre, Vito Trianni, Francesco Mondada, Marco Dorigo

Video ID : 115

Each robot is provided with an independent neural controller which determines the desired speed of the two wheels on the basis of the traction force caused by the movements of the other robots. The evolved robots are able to display coordinated-motion capability, independent from the way in which they are assembled, as well as to coordinate in carrying heavy objects.

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.

Extracting kinematic background knowledge from interactions using task-sensitive, relational learning

Author  Sebastian Hofer, Tobias Lang, Oliver Brock

Video ID : 671

To successfully manipulate novel objects, robots must first acquire information about the objects' kinematic structure. We present a method to learn relational, kinematic, background knowledge from exploratory interactions with the world. As the robot gathers experience, this background knowledge enables the acquisition of kinematic world models with increasing efficiency. Learning such background knowledge, however, proves difficult, especially in complex, feature-rich domains. We present a novel, task-sensitive, relational-rule learner and demonstrate that it is able to learn accurate kinematic background knowledge in domains where other approaches fail. The resulting background knowledge is more compact and generalizes better than that obtained with existing approaches.

Chapter 26 — Flying Robots

Stefan Leutenegger, Christoph Hürzeler, Amanda K. Stowers, Kostas Alexis, Markus W. Achtelik, David Lentink, Paul Y. Oh and Roland Siegwart

Unmanned aircraft systems (UASs) have drawn increasing attention recently, owing to advancements in related research, technology, and applications. While having been deployed successfully in military scenarios for decades, civil use cases have lately been tackled by the robotics research community.

This chapter overviews the core elements of this highly interdisciplinary field; the reader is guided through the design process of aerial robots for various applications starting with a qualitative characterization of different types of UAS. Design and modeling are closely related, forming a typically iterative process of drafting and analyzing the related properties. Therefore, we overview aerodynamics and dynamics, as well as their application to fixed-wing, rotary-wing, and flapping-wing UAS, including related analytical tools and practical guidelines. Respecting use-case-specific requirements and core autonomous robot demands, we finally provide guidelines to related system integration challenges.

The astounding athletic power of quadcopters

Author  Raffaello D'Andrea

Video ID : 694

In a robot lab at TEDGlobal, Raffaello D'Andrea demonstrates his flying quadcopters: Robots that think like athletes, solving physical problems with algorithms that help them learn. In a series of nifty demos, D'Andrea works with drones that play catch, balance and make decisions together -- and watch out for an I-want-this-now demo of Kinect-controlled quads.

Chapter 43 — Telerobotics

Günter Niemeyer, Carsten Preusche, Stefano Stramigioli and Dongjun Lee

In this chapter we present an overview of the field of telerobotics with a focus on control aspects. To acknowledge some of the earliest contributions and motivations the field has provided to robotics in general, we begin with a brief historical perspective and discuss some of the challenging applications. Then, after introducing and classifying the various system architectures and control strategies, we emphasize bilateral control and force feedback. This particular area has seen intense research work in the pursuit of telepresence. We also examine some of the emerging efforts, extending telerobotic concepts to unconventional systems and applications. Finally,we suggest some further reading for a closer engagement with the field.

Single- and dual-arm supervisory and shared control

Author  Paul S. Schenker, Antal K. Bejczy, Won S. Kim

Video ID : 299

This video shows single- and dual-arm supervisory and shared teleoperation control for the remote repair of solar panels attached to a space satellite.

Chapter 51 — Modeling and Control of Underwater Robots

Gianluca Antonelli, Thor I. Fossen and Dana R. Yoerger

This chapter deals with modeling and control of underwater robots. First, a brief introduction showing the constantly expanding role of marine robotics in oceanic engineering is given; this section also contains some historical backgrounds. Most of the following sections strongly overlap with the corresponding chapters presented in this handbook; hence, to avoid useless repetitions, only those aspects peculiar to the underwater environment are discussed, assuming that the reader is already familiar with concepts such as fault detection systems when discussing the corresponding underwater implementation. Themodeling section is presented by focusing on a coefficient-based approach capturing the most relevant underwater dynamic effects. Two sections dealing with the description of the sensor and the actuating systems are then given. Autonomous underwater vehicles require the implementation of mission control system as well as guidance and control algorithms. Underwater localization is also discussed. Underwater manipulation is then briefly approached. Fault detection and fault tolerance, together with the coordination control of multiple underwater vehicles, conclude the theoretical part of the chapter. Two final sections, reporting some successful applications and discussing future perspectives, conclude the chapter. The reader is referred to Chap. 25 for the design issues.

The Icebot

Author  Woods Hole Oceanographic Institution

Video ID : 92

A team of scientists field-tests an autonomous underwater vehicle, sending it into a hole in an ice floe off the coast of Alaska ... and hoping they can get it back.

Chapter 43 — Telerobotics

Günter Niemeyer, Carsten Preusche, Stefano Stramigioli and Dongjun Lee

In this chapter we present an overview of the field of telerobotics with a focus on control aspects. To acknowledge some of the earliest contributions and motivations the field has provided to robotics in general, we begin with a brief historical perspective and discuss some of the challenging applications. Then, after introducing and classifying the various system architectures and control strategies, we emphasize bilateral control and force feedback. This particular area has seen intense research work in the pursuit of telepresence. We also examine some of the emerging efforts, extending telerobotic concepts to unconventional systems and applications. Finally,we suggest some further reading for a closer engagement with the field.

Semi-autonomous teleoperation of multiple UAVs: Tumbling over an obstacle

Author  Antonio Franchi, Paolo Robuffo Giordano

Video ID : 72

This video shows the bilateral teleoperation of a group of four quadrotor UAVs navigating in a cluttered environment. The human operator provides velocity-level motion commands and receives force-feedback information on the UAV interaction with the environment (e.g., presence of obstacles and external disturbances).

Chapter 19 — Robot Hands

Claudio Melchiorri and Makoto Kaneko

Multifingered robot hands have a potential capability for achieving dexterous manipulation of objects by using rolling and sliding motions. This chapter addresses design, actuation, sensing and control of multifingered robot hands. From the design viewpoint, they have a strong constraint in actuator implementation due to the space limitation in each joint. After briefly introducing the overview of anthropomorphic end-effector and its dexterity in Sect. 19.1, various approaches for actuation are provided with their advantages and disadvantages in Sect. 19.2. The key classification is (1) remote actuation or build-in actuation and (2) the relationship between the number of joints and the number of actuator. In Sect. 19.3, actuators and sensors used for multifingered hands are described. In Sect. 19.4, modeling and control are introduced by considering both dynamic effects and friction. Applications and trends are given in Sect. 19.5. Finally, this chapter is closed with conclusions and further reading.

The Dexmart Hand

Author  Claudio Melchiorri

Video ID : 767

Grasp and manipulation tasks executed by the Dexmart Hand, an anthropomorphic robot hand developed within an European research activity. Detailed aspects of the "twisted-spring" actuation principle are demonstrated.

Chapter 71 — Cognitive Human-Robot Interaction

Bilge Mutlu, Nicholas Roy and Selma Šabanović

A key research challenge in robotics is to design robotic systems with the cognitive capabilities necessary to support human–robot interaction. These systems will need to have appropriate representations of the world; the task at hand; the capabilities, expectations, and actions of their human counterparts; and how their own actions might affect the world, their task, and their human partners. Cognitive human–robot interaction is a research area that considers human(s), robot(s), and their joint actions as a cognitive system and seeks to create models, algorithms, and design guidelines to enable the design of such systems. Core research activities in this area include the development of representations and actions that allow robots to participate in joint activities with people; a deeper understanding of human expectations and cognitive responses to robot actions; and, models of joint activity for human–robot interaction. This chapter surveys these research activities by drawing on research questions and advances from a wide range of fields including computer science, cognitive science, linguistics, and robotics.

Robotic secrets revealed, Episode 1

Author  Greg Trafton

Video ID : 129

A Naval Research Laboratory (NRL) scientist shows a magic trick to a mobile-dextrous-social robot, demonstrating the robot's use and interpretation of gestures. The video highlights recent gesture-recognition work and NRL's novel cognitive architecture, ACT-R/E. While set within a popular game of skill, this video illustrates several Navy-relevant issues, including computational cognitive architecture which enables autonomous function, and integrates perceptual information with higher-level cognitive reasoning, gesture recognition for shoulder-to-shoulder human-robot interaction, and anticipation and learning on a robotic system. Such abilities will be critical for future, naval, autonomous systems for persistent surveillance, tactical mobile robots, and other autonomous platforms.