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Chapter 80 — Roboethics: Social and Ethical Implications

Gianmarco Veruggio, Fiorella Operto and George Bekey

This chapter outlines the main developments of roboethics 9 years after a worldwide debate on the subject – that is, the applied ethics about ethical, legal, and societal aspects of robotics – opened up. Today, roboethics not only counts several thousands of voices on the Web, but is the issue of important literature relating to almost all robotics applications, and of hundreds of rich projects, workshops, and conferences. This increasing interest and sometimes even fierce debate expresses the perception and need of scientists, manufacturers, and users of professional guidelines and ethical indications about robotics in society.

Some of the issues presented in the chapter are well known to engineers, and less known or unknown to scholars of humanities, and vice versa. However, because the subject is transversal to many disciplines, complex, articulated, and often misrepresented, some of the fundamental concepts relating to ethics in science and technology are recalled and clarified.

A detailed taxonomy of sensitive areas is presented. It is based on a study of several years and referred to by scientists and scholars, the result of which is the Euron Roboethics Roadmap. This taxonomy identifies themost evident/urgent/sensitive ethical problems in the main applicative fields of robotics, leaving more in-depth research to further studies.

Roboethics: Prosthesis

Author  Fiorella Operto

Video ID : 774

Ethical, legal and societal issues in medical robotics. Bionic implants and prosthetics can be used to restore human capabilities and functions. Applications range from human prostheses for locomotion, manipulation, vision, sensing, and other functions: Artificial limbs (legs and arms; artificial internal organs (heart, kidney); artificial senses (eyes, ears...); human augmentation (exoskeletons). This field has an important connection with neuroscience to develop neural interfaces and sensory-motor coordination systems for the integration of these bionic devices with the human body/brain. The very distinction between restoring and enhancing is problematic in some cases insofar as interventions on the human body may have a variety of possibly unpredictable side-effects. Social and economic discrimination towards human beings may arise as a consequence of the enhanced physical and mental properties of super-human cyborgs.

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.

Admittance control of a human-centered 3-DOF robotic arm using dfferential elastic actuators

Author  Marc-Antoine Legault, Marc-Antoine Lavoie, Francois Cabana, Philippe Jacob-Goudreau, Dominic Létourneau, François Michaud

Video ID : 610

This video shows the functionalities of a three-serial-DOF robotic arm where each DOF is actuated with a patent-pending differential elastic actuator (DEA). A DEA uses differential coupling between a high-impedance mechanical speed source and a low-impedance mechanical spring. A passive torsion spring (thus the name elastic), with a known impedance characteristic corresponding to the spring stiffness, is used, with an electrical DC brushless motor. A non-turning sensor connected in series with the spring measures the torque output of the actuator. Reference: M.-A. Legault, M.-A. Lavoie, F. Cabana, P. Jacob-Goudreau, D. Létourneau, F. Michaud: Admittance control of a human centered 3-DOF robotic arm using differential elastic actuators , Proc. IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Nice (2008), pp. 4143–4144; doi: 10.1109/IROS.2008.4651039.

Chapter 17 — Limbed Systems

Shuuji Kajita and Christian Ott

A limbed system is a mobile robot with a body, legs and arms. First, its general design process is discussed in Sect. 17.1. Then we consider issues of conceptual design and observe designs of various existing robots in Sect. 17.2. As an example in detail, the design of a humanoid robot HRP-4C is shown in Sect. 17.3. To design a limbed system of good performance, it is important to take into account of actuation and control, like gravity compensation, limit cycle dynamics, template models, and backdrivable actuation. These are discussed in Sect. 17.4.

In Sect. 17.5, we overview divergence of limbed systems. We see odd legged walkers, leg–wheel hybrid robots, leg–arm hybrid robots, tethered walking robots, and wall-climbing robots. To compare limbed systems of different configurations,we can use performance indices such as the gait sensitivity norm, the Froude number, and the specific resistance, etc., which are introduced in Sect. 17.6.

RHex rough-terrain robot

Author  Boston Dynamics

Video ID : 536

A leg-wheel hybrid robot RHex developed by Boston Dynamics.

Chapter 23 — Biomimetic Robots

Kyu-Jin Cho and Robert Wood

Biomimetic robot designs attempt to translate biological principles into engineered systems, replacing more classical engineering solutions in order to achieve a function observed in the natural system. This chapter will focus on mechanism design for bio-inspired robots that replicate key principles from nature with novel engineering solutions. The challenges of biomimetic design include developing a deep understanding of the relevant natural system and translating this understanding into engineering design rules. This often entails the development of novel fabrication and actuation to realize the biomimetic design.

This chapter consists of four sections. In Sect. 23.1, we will define what biomimetic design entails, and contrast biomimetic robots with bio-inspired robots. In Sect. 23.2, we will discuss the fundamental components for developing a biomimetic robot. In Sect. 23.3, we will review detailed biomimetic designs that have been developed for canonical robot locomotion behaviors including flapping-wing flight, jumping, crawling, wall climbing, and swimming. In Sect. 23.4, we will discuss the enabling technologies for these biomimetic designs including material and fabrication.

A single-motor-actuated, miniature, steerable jumping robot

Author  Jianguo Zhao, Jing Xu, Bingtuan Gao, Ning Xi, Fernando J. Cintron, Matt W. Mutka, Li Xiao

Video ID : 280

The contents of the video are divided into three parts. The first part illustrates the individual functions of the robot such as jumping, self-righting and steering. The second part demonstrates the robot's locomotion capability in indoor environments. Scenarios such as jumping from the floor, jumping in an office and jumping over stairs are included. The third part shows the robot's locomotion capability in outdoor environments. Experiments on uneven ground, ground with small gravels and ground with grass are included.

Chapter 15 — Robot Learning

Jan Peters, Daniel D. Lee, Jens Kober, Duy Nguyen-Tuong, J. Andrew Bagnell and Stefan Schaal

Machine learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors; conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in robot learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this chapter, we attempt to strengthen the links between the two research communities by providing a survey of work in robot learning for learning control and behavior generation in robots. We highlight both key challenges in robot learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our chapter lies on model learning for control and robot reinforcement learning. We demonstrate how machine learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

Inverted helicopter hovering

Author  Pieter Abbeel

Video ID : 352

An example of simulation-based optimization using a learned forward model. This brief video shows a successful application of reinforcement learning to the design of a controller for sustained inverted flight of an autonomous helicopter. The authors began by learning a stochastic, nonlinear forward model of the helicopter’s dynamics. Then, a reinforcement learning algorithm was applied to automatically learn a controller for autonomous inverted hovering. The video illustrates Section 15.2.5 -- Applications of Model Learning, Springer Handbook of Robotics, 2nd ed (2016); Reference: A.Y. Ng, A. Coates, M. Diel, V. Ganapathi, J. Schulte, B. Tse, E. Berger, E. Liang: Autonomous inverted helicopter flight via reinforcement learning, IX Int. Symp. Exp. Robot. 2004, Springer Tract. Adv. Robot. 21, 363-372 (2006)

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.

Human-robot jazz improvisation

Author  Guy Hoffman

Video ID : 236

The stage debut of Shimon, the robotic marimba player. Also, the world's first human-robot rendition of Duke Jordan's "Jordu", for human piano and robot marimba.

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.

Generation of human-care behaviors by human-interactive robot RI-MAN

Author  Masaki Onishi, Tadashi Odashima, Shinya Hirano, Kenji Tahara, Toshiharu Mukai

Video ID : 607

This video shows the the realization of environmental interactive tasks, such as human-care tasks, by replaying the human motion repeatedly. A novel motion-generation approach is shown to integrate the cognitive information into the mimicking of human motions so as to realize the final complex task by the robot. Reference: M. Onishi, Z.W. Luo, T. Odashima, S. Hirano, K. Tahara, T. Mukai: Generation of human care behaviors by human-interactive robot RI-MAN, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Rome (2007), pp. 3128-3129; doi: 10.1109/ROBOT.2007.363950.

Chapter 74 — Learning from Humans

Aude G. Billard, Sylvain Calinon and Rüdiger Dillmann

This chapter surveys the main approaches developed to date to endow robots with the ability to learn from human guidance. The field is best known as robot programming by demonstration, robot learning from/by demonstration, apprenticeship learning and imitation learning. We start with a brief historical overview of the field. We then summarize the various approaches taken to solve four main questions: when, what, who and when to imitate. We emphasize the importance of choosing well the interface and the channels used to convey the demonstrations, with an eye on interfaces providing force control and force feedback. We then review algorithmic approaches to model skills individually and as a compound and algorithms that combine learning from human guidance with reinforcement learning. We close with a look on the use of language to guide teaching and a list of open issues.

Demonstration by teleoperation of humanoid HRP-2

Author  Sylvain Calinon, Paul Evrard, Elena Gribovskaya, Aude Billard, Abderrahmane Kheddar

Video ID : 101

Demonstration by teleoperation of the HRP-2 humanoid robot. Reference: S. Calinon, P. Evrard, E. Gribovskaya, A.G. Billard, A. Kheddar: Learning collaborative manipulation tasks by demonstration using a haptic interface, Proc. Intl Conf. Adv. Robot. (ICAR), (2009), pp. 1–6; URL: http://programming-by-demonstration.org/showVideo.php?video=10 .

Chapter 23 — Biomimetic Robots

Kyu-Jin Cho and Robert Wood

Biomimetic robot designs attempt to translate biological principles into engineered systems, replacing more classical engineering solutions in order to achieve a function observed in the natural system. This chapter will focus on mechanism design for bio-inspired robots that replicate key principles from nature with novel engineering solutions. The challenges of biomimetic design include developing a deep understanding of the relevant natural system and translating this understanding into engineering design rules. This often entails the development of novel fabrication and actuation to realize the biomimetic design.

This chapter consists of four sections. In Sect. 23.1, we will define what biomimetic design entails, and contrast biomimetic robots with bio-inspired robots. In Sect. 23.2, we will discuss the fundamental components for developing a biomimetic robot. In Sect. 23.3, we will review detailed biomimetic designs that have been developed for canonical robot locomotion behaviors including flapping-wing flight, jumping, crawling, wall climbing, and swimming. In Sect. 23.4, we will discuss the enabling technologies for these biomimetic designs including material and fabrication.

HAMR3: An autonomous 1.7 g ambulatory robot

Author  Andrew T. Baisch, Christian Heimlich, Michael Karpelson, Robert J. Wood

Video ID : 406

The successor to HAMR2, HAMR3 is a cockroach-inspired robot developed at the Harvard Microrobotics Lab by Andrew Baisch, Christian Heimlich, Michael Karpelson and Robert J. Wood. This version of the robot includes fully-integrated, onboard power electronics.

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.

Influence of response time

Author  Takayuki Kanda

Video ID : 806

This video illustrates the importance of response time in interactions with a social robot. In the first part of the study, it was revealed that it is hard to wait for more than two seconds. In the second part of the study, a technique to use a "conversational filler" is developed, which moderates the frustrations of waiting too long.