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Chapter 78 — Perceptual Robotics

Heinrich Bülthoff, Christian Wallraven and Martin A. Giese

Robots that share their environment with humans need to be able to recognize and manipulate objects and users, perform complex navigation tasks, and interpret and react to human emotional and communicative gestures. In all of these perceptual capabilities, the human brain, however, is still far ahead of robotic systems. Hence, taking clues from the way the human brain solves such complex perceptual tasks will help to design better robots. Similarly, once a robot interacts with humans, its behaviors and reactions will be judged by humans – movements of the robot, for example, should be fluid and graceful, and it should not evoke an eerie feeling when interacting with a user. In this chapter, we present Perceptual Robotics as the field of robotics that takes inspiration from perception research and neuroscience to, first, build better perceptual capabilities into robotic systems and, second, to validate the perceptual impact of robotic systems on the user.

Active in-hand object recognition

Author  Christian Wallraven

Video ID : 569

This video showcases the implementation of active object learning and recognition using the framework proposed in Browatzki et al. [1, 2]. The first phase shows the robot trying to learn the visual representation of several paper cups differing by a few key features. The robot executes a pre-programmed exploration program to look at the cup from all sides. The (very low-resolution) visual input is tracked and so-called key-frames are extracted which represent the (visual) exploration. After learning, the robot tries to recognize cups that have been placed into its hands using a similar exploration program based on visual information - due to the low-resolution input and the highly similar objects, the robot, however, fails to make the correct decision. The video then shows the second, advanced, exploration, which is based on actively seeking the view that is expected to provide maximum information about the object. For this, the robot embeds the learned visual information into a proprioceptive map indexed by the two joint angles of the hand. In this map, the robot now tries to predict the joint-angle combination that provides the most information about the object, given the current state of exploration. The implementation uses particle filtering to track a large number of object (view) hypotheses at the same time. Since the robot now uses a multisensory representation, the subsequent object-recognition trials are all correct, despite poor visual input and highly similar objects. References: [1] B Browatzki, V. Tikhanoff, G. Metta, H.H. Bülthoff, C. Wallraven: Active in-hand object recognition on a humanoid robot, IEEE Trans. Robot. 30(5), 1260-1269 (2014); [2] B. Browatzki, V. Tikhanoff, G. Metta, H.H. Bülthoff, C. Wallraven: Active object recognition on a humanoid robot, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), St. Paul (2012), pp. 2021-2028.

Chapter 61 — Robot Surveillance and Security

Wendell H. Chun and Nikolaos Papanikolopoulos

This chapter introduces the foundation for surveillance and security robots for multiple military and civilian applications. The key environmental domains are mobile robots for ground, aerial, surface water, and underwater applications. Surveillance literallymeans to watch fromabove,while surveillance robots are used to monitor the behavior, activities, and other changing information that are gathered for the general purpose of managing, directing, or protecting one’s assets or position. In a practical sense, the term surveillance is taken to mean the act of observation from a distance, and security robots are commonly used to protect and safeguard a location, some valuable assets, or personal against danger, damage, loss, and crime. Surveillance is a proactive operation,while security robots are a defensive operation. The construction of each type of robot is similar in nature with amobility component, sensor payload, communication system, and an operator control station.

After introducing the major robot components, this chapter focuses on the various applications. More specifically, Sect. 61.3 discusses the enabling technologies of mobile robot navigation, various payload sensors used for surveillance or security applications, target detection and tracking algorithms, and the operator’s robot control console for human–machine interface (HMI). Section 61.4 presents selected research activities relevant to surveillance and security, including automatic data processing of the payload sensors, automaticmonitoring of human activities, facial recognition, and collaborative automatic target recognition (ATR). Finally, Sect. 61.5 discusses future directions in robot surveillance and security, giving some conclusions and followed by references.

Security: Facial recognition

Author  Ali Mollahosseini, Mohammad Mahoor

Video ID : 553

Video of face tracking and facial-landmark-point extraction of Ali's face for a security robot.

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.

Justin: A humanoid upper body system for two-handed manipulation experiments

Author  Christoph Borst, Christian Ott, Thomas Wimböck, Bernhard Brunner, Franziska Zacharias, Berthold Bäuml

Video ID : 626

This video presents a humanoid two-arm system developed as a research platform for studying dexterous two-handed manipulation. The system is based on the modular DLR-Lightweight-Robot-III and the DLR-Hand-II. Two arms and hands are combined with a 3-DOF movable torso and a visual system to form a complete humanoid upper body. The diversity of the system is demonstrated by showing the mechanical design, several control concepts, the application of rapid prototyping and hardware-in-the-loop (HIL) development, as well as two-handed manipulation experiments and the integration of path planning capabilities.

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.

VSA-Cube arm: Drawing on a wavy surface (selective stiffness)

Author  Centro di Ricerca "E. Piaggio"

Video ID : 474

A 3-DOF arm, built with VSA-cube units, performing a circle on a wavy surface with a proper (selective) stiffness preset.

Chapter 62 — Intelligent Vehicles

Alberto Broggi, Alex Zelinsky, Ümit Özgüner and Christian Laugier

This chapter describes the emerging robotics application field of intelligent vehicles – motor vehicles that have autonomous functions and capabilities. The chapter is organized as follows. Section 62.1 provides a motivation for why the development of intelligent vehicles is important, a brief history of the field, and the potential benefits of the technology. Section 62.2 describes the technologies that enable intelligent vehicles to sense vehicle, environment, and driver state, work with digital maps and satellite navigation, and communicate with intelligent transportation infrastructure. Section 62.3 describes the challenges and solutions associated with road scene understanding – a key capability for all intelligent vehicles. Section 62.4 describes advanced driver assistance systems, which use the robotics and sensing technologies described earlier to create new safety and convenience systems for motor vehicles, such as collision avoidance, lane keeping, and parking assistance. Section 62.5 describes driver monitoring technologies that are being developed to mitigate driver fatigue, inattention, and impairment. Section 62.6 describes fully autonomous intelligent vehicles systems that have been developed and deployed. The chapter is concluded in Sect. 62.7 with a discussion of future prospects, while Sect. 62.8 provides references to further reading and additional resources.

Motion prediction using the Bayesian-occupancy-filter approach (Inria)

Author  Christian Laugier, E-Motion Team

Video ID : 420

This video illustrates the prediction capabilities of the Bayesian-occupancy-filter approach which is able to maintain an updated record and estimate of the relatives positions and velocities of an autonomous vehicle and of a detected-and-tracked moving obstacle (e.g., a pedestrian in the video). The approach still works despite temporary obstructions. The method has been patented in, and commercialized since, 2005. More details in [62.60].

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.

Hammering task with the DLR Hand Arm System

Author  Markus Grebenstein, Alin Albu-Schäffer, Thomas Bahls, Maxime Chalon, Oliver Eiberger, Werner Friedl, Robin Gruber, Sami Haddadin, Ulrich Hagn, Robert Haslinger, Hannes Höppner, Stefan Jörg, Mathias Nickl, Alexander Nothhelfer, Florian Petit, Josef Rei

Video ID : 464

The DLR Hand Arm System uses a hammer to drive a nail into a wooden board. The passive flexibility in the variable stiffness actuators (VSA) helps to keep a stable grasp during the impact and protects the hardware from damage.

Chapter 62 — Intelligent Vehicles

Alberto Broggi, Alex Zelinsky, Ümit Özgüner and Christian Laugier

This chapter describes the emerging robotics application field of intelligent vehicles – motor vehicles that have autonomous functions and capabilities. The chapter is organized as follows. Section 62.1 provides a motivation for why the development of intelligent vehicles is important, a brief history of the field, and the potential benefits of the technology. Section 62.2 describes the technologies that enable intelligent vehicles to sense vehicle, environment, and driver state, work with digital maps and satellite navigation, and communicate with intelligent transportation infrastructure. Section 62.3 describes the challenges and solutions associated with road scene understanding – a key capability for all intelligent vehicles. Section 62.4 describes advanced driver assistance systems, which use the robotics and sensing technologies described earlier to create new safety and convenience systems for motor vehicles, such as collision avoidance, lane keeping, and parking assistance. Section 62.5 describes driver monitoring technologies that are being developed to mitigate driver fatigue, inattention, and impairment. Section 62.6 describes fully autonomous intelligent vehicles systems that have been developed and deployed. The chapter is concluded in Sect. 62.7 with a discussion of future prospects, while Sect. 62.8 provides references to further reading and additional resources.

VIAC: The VisLab Intercontinental Autonomous Challenge

Author  Alberto Broggi

Video ID : 179

This is the official presentation of the VisLab Intercontinental Autonomous Challenge, the longest ever trip undertaken with driverless vehicles, from Italy to China. Four electric vehicles left Parma (Italy) on July 26, 2010, and arrived in Shanghai on Oct 28, 2010, after 3 months of driving and more than 15,000 km. Check www.viac.vislab.it for details.

Chapter 67 — Humanoids

Paul Fitzpatrick, Kensuke Harada, Charles C. Kemp, Yoshio Matsumoto, Kazuhito Yokoi and Eiichi Yoshida

Humanoid robots selectively immitate aspects of human form and behavior. Humanoids come in a variety of shapes and sizes, from complete human-size legged robots to isolated robotic heads with human-like sensing and expression. This chapter highlights significant humanoid platforms and achievements, and discusses some of the underlying goals behind this area of robotics. Humanoids tend to require the integration ofmany of the methods covered in detail within other chapters of this handbook, so this chapter focuses on distinctive aspects of humanoid robotics with liberal cross-referencing.

This chapter examines what motivates researchers to pursue humanoid robotics, and provides a taste of the evolution of this field over time. It summarizes work on legged humanoid locomotion, whole-body activities, and approaches to human–robot communication. It concludes with a brief discussion of factors that may influence the future of humanoid robots.

Regrasp planning for pivoting manipulation by a humanoid robot

Author  Eiichi Yoshida

Video ID : 599

The pivoting manipulation presented in video 597 is extended for the humanoid robot to carry a bulky object in a constrained environment. Using multiple roadmaps with different grasping positions and free walking motions, the humanoid robot can set down the object near narrow places and then regrasp it from another position to move the object to the goal.

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.

Development of a versatile underwater robot - GTS ROV ALPHA

Author  Georgia Tech Savannah Robotics

Video ID : 790

This underwater vehicle won the award for design elegance at the 2009 MATE International ROV competition. In November 2009, it was deployed from the R/V Savannah for an initial sea trial. In the future, it is intended to serve as a platform for underwater manipulation, mapping, and control experiments.