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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.

Visual communicative nonverbal behaviors of the Sunflower robot

Author  Kerstin Dautenhahn

Video ID : 219

The video illustrates the experiments as described in Koay et. al (2013). The Sunflower robot, developed by Kheng Lee Koay at the University of Hertfordshire, is a non-humanoid robot, using communicative signals inspired by dog-human interaction. The biological behaviors had been abstracted and translated to the specific robot embodiment. The results show that the robot is able to communicate its intention to a person and encourages the participant to attend to events and locations in a home environment. The work has been part of the of the European project LIREC (http://lirec.eu/project).

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.

Inverse reinforcement

Author  Pieter Abbeel

Video ID : 353

This video shows a successful example of inverse reinforcement learning for acrobatic helicopter maneuvers. It illustrates apprenticeship learning algorithms, which leverage expert demonstrations to efficiently learn good controllers for tasks as demonstrated by an expert. The experimental results captured here include the first autonomous execution of a wide range of maneuvers and a complete airshow. The controllers perform as well as, and often even better than, the human expert pilot. The video illustrates a solution to the "Curse of Goal Specification" in Sect 15.3.6 Challenges in Robot Reinforcement Learning. Reference: P. Abbeel, A. Coates, A.Y. Ng: Autonomous helicopter aerobatics through apprenticeship learning, Int. J. Robot. Res. 29(13), 1608–1639 (2010)

Chapter 49 — Modeling and Control of Wheeled Mobile Robots

Claude Samson, Pascal Morin and Roland Lenain

This chaptermay be seen as a follow up to Chap. 24, devoted to the classification and modeling of basic wheeled mobile robot (WMR) structures, and a natural complement to Chap. 47, which surveys motion planning methods for WMRs. A typical output of these methods is a feasible (or admissible) reference state trajectory for a given mobile robot, and a question which then arises is how to make the physical mobile robot track this reference trajectory via the control of the actuators with which the vehicle is equipped. The object of the present chapter is to bring elements of the answer to this question based on simple and effective control strategies.

The chapter is organized as follows. Section 49.2 is devoted to the choice of controlmodels and the determination of modeling equations associated with the path-following control problem. In Sect. 49.3, the path following and trajectory stabilization problems are addressed in the simplest case when no requirement is made on the robot orientation (i. e., position control). In Sect. 49.4 the same problems are revisited for the control of both position and orientation. The previously mentionned sections consider an ideal robot satisfying the rolling-without-sliding assumption. In Sect. 49.5, we relax this assumption in order to take into account nonideal wheel-ground contact. This is especially important for field-robotics applications and the proposed results are validated through full scale experiments on natural terrain. Finally, a few complementary issues on the feedback control of mobile robots are briefly discussed in the concluding Sect. 49.6, with a list of commented references for further reading on WMRs motion control.

Tracking of an admissible trajectory with a car-like vehicle

Author  Pascal Morin, Claude Samson

Video ID : 181

This is an animation showing the tracking of an admissible reference trajectory (red vehicle) with a car-like vehicle (green vehicle). The robot is able to ensure asymptotic convergence of the tracking error to zero, based on the feedback controller presented in Chap. 49.4, Springer Handbook of Robotics, 2nd edn (2016).

Chapter 24 — Wheeled Robots

Woojin Chung and Karl Iagnemma

The purpose of this chapter is to introduce, analyze, and compare various wheeled mobile robots (WMRs) and to present several realizations and commonly encountered designs. The mobility of WMR is discussed on the basis of the kinematic constraints resulting from the pure rolling conditions at the contact points between the wheels and the ground. Practical robot structures are classified according to the number of wheels, and features are introduced focusing on commonly adopted designs. Omnimobile robot and articulated robots realizations are described. Wheel–terrain interaction models are presented in order to compute forces at the contact interface. Four possible wheel-terrain interaction cases are shown on the basis of relative stiffness of the wheel and terrain. A suspension system is required to move on uneven surfaces. Structures, dynamics, and important features of commonly used suspensions are explained.

An omnidirectional robot with four mecanum wheels

Author  Nexus Automation Limited

Video ID : 327

This video shows a holonomic omnidirectional mobile robot with four mecanum wheels. The mecanum wheel is similar to the Swedish wheel. The rollers of the mecanum wheel have an axis of rotation at 45° to the axis of the wheel hub rotation. The design problem of omnidirectional robots becomes easier because the rotating axes of all wheel hubs can be placed in parallel.

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.

BLUE: A biped robot with variable stiffness and damping

Author  Chengxu Zhou, Xin Wang, Zhibin Li, Nikolaos Tsagarakis

Video ID : 466

The video includes clips of the robot walking, squatting while displaying various degrees of stiffness, and the stiffness adjustment mechanism.

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 47 — Motion Planning and Obstacle Avoidance

Javier Minguez, Florant Lamiraux and Jean-Paul Laumond

This chapter describes motion planning and obstacle avoidance for mobile robots. We will see how the two areas do not share the same modeling background. From the very beginning of motion planning, research has been dominated by computer sciences. Researchers aim at devising well-grounded algorithms with well-understood completeness and exactness properties.

The challenge of this chapter is to present both nonholonomic motion planning (Sects. 47.1–47.6) and obstacle avoidance (Sects. 47.7–47.10) issues. Section 47.11 reviews recent successful approaches that tend to embrace the whole problemofmotion planning and motion control. These approaches benefit from both nonholonomic motion planning and obstacle avoidance methods.

Mobile-robot navigation system in outdoor pedestrian environment

Author  Chin-Kai Chang

Video ID : 711

We present a mobile-robot navigation system guided by a novel vision-based, road-recognition approach. The system represents the road as a set of lines extrapolated from the detected image contour segments. These lines enable the robot to maintain its heading by centering the vanishing point in its field of view, and to correct the long-term drift from its original lateral position. We integrate odometry and our visual, road-recognition system into a grid-based local map which estimates the robot pose as well as its surroundings to generate a movement path. Our road recognition system is able to estimate the road center on a standard dataset with 25 076 images to within 11.42 cm (with respect to roads that are at least 3 m wide). It outperforms three other state-of-the-art systems. In addition, we extensively test our navigation system in four busy campus environments using a wheeled robot. Our tests cover more than 5 km of autonomous driving on a busy college campus without failure. This demonstrates the robustness of the proposed approach to handle challenges including occlusion by pedestrians, non-standard complex road markings and shapes, shadows, and miscellaneous obstacle 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.

DLR's Agile Justin plays catch with Rollin' Justin

Author  DLR

Video ID : 661

DLR has developed a new robot named Agile Justin that is capable of tossing a baseball. This seemed like a natural complement to Rollin' Justin's ability to catch a baseball, so they teamed them up for a friendly game of "catch."

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.

Safety evaluation of lightweight robots

Author  Sami Haddadin

Video ID : 463

Inteview with Sami Haddadin on safety evaluation of lightweight robots (Discovery Channel interview "Daily Planet", January 9th, 2008)

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.

Atlas whole-body grasping

Author  DRC Team MIT

Video ID : 651

A simple demonstration of automated perception, whole-body motion planning, and dynamic stabilization using Atlas and software developed at MIT.