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Chapter 10 — Redundant Robots

Stefano Chiaverini, Giuseppe Oriolo and Anthony A. Maciejewski

This chapter focuses on redundancy resolution schemes, i. e., the techniques for exploiting the redundant degrees of freedom in the solution of the inverse kinematics problem. This is obviously an issue of major relevance for motion planning and control purposes.

In particular, task-oriented kinematics and the basic methods for its inversion at the velocity (first-order differential) level are first recalled, with a discussion of the main techniques for handling kinematic singularities. Next, different firstorder methods to solve kinematic redundancy are arranged in two main categories, namely those based on the optimization of suitable performance criteria and those relying on the augmentation of the task space. Redundancy resolution methods at the acceleration (second-order differential) level are then considered in order to take into account dynamics issues, e.g., torque minimization. Conditions under which a cyclic task motion results in a cyclic joint motion are also discussed; this is a major issue when a redundant manipulator is used to execute a repetitive task, e.g., in industrial applications. The use of kinematic redundancy for fault tolerance is analyzed in detail. Suggestions for further reading are given in a final section.

Human inspired tele-impedance and minimum-effort controller for improved manipulation Performance‬

Author  IIT Videos

Video ID : 815

Humans incorporate and switch between learnt neuro-motor strategies while performing complex tasks. To this purpose, kinematic redundancy is exploited in order to achieve optimized performance. Inspired by the superior motor skills of humans, in this work, we investigate a combined free-motion and contact-efficient controller in a certain class of robotic manipulation. In this multiple-criteria controller, kinematic degrees of redundancy are adapted according to task-suitable dynamic costs. The proposed algorithm attributes high priority to a minimum-effort controller while performing point-to-point, free-space movements. Once the robot comes into contact with the environment, the tele-impedance, common mode stiffness (CMS)-configuration dependent stiffness (CDS) controller will replicate the human's estimated endpoint stiffness and measured equilibrium-position profiles in the slave robotic arm, in real-time.

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.

Learning compliant motion from human demonstration

Author  Aude Billard

Video ID : 478

This video illustrates how one can teach a robot to display the right amount of stiffness to perform a task successfully. Decrease in stiffness is demonstrated by shaking the robot, while increase in stiffness is conveyed by pressing on the robot's arm (pressure being measured through tactile sensors along the robot's arm). Reference: K. Kronander,A. Billard: Learning compliant manipulation through kinesthetic and tactile human-robot interaction, IEEE Trans. Haptics 7(3), 367-380 (2013); doi: 10.1109/TOH.2013.54 .

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.

Promotional video of robot for cleaning up Fukushima

Author  James P. Trevelyan

Video ID : 583

Many companies have proposed new robots to help with the Fukushima reactor decommissioning process. This is one of many such promotional videos.

Chapter 68 — Human Motion Reconstruction

Katsu Yamane and Wataru Takano

This chapter presents a set of techniques for reconstructing and understanding human motions measured using current motion capture technologies. We first review modeling and computation techniques for obtaining motion and force information from human motion data (Sect. 68.2). Here we show that kinematics and dynamics algorithms for articulated rigid bodies can be applied to human motion data processing, with help from models based on knowledge in anatomy and physiology. We then describe methods for analyzing human motions so that robots can segment and categorize different behaviors and use them as the basis for human motion understanding and communication (Sect. 68.3). These methods are based on statistical techniques widely used in linguistics. The two fields share the common goal of converting continuous and noisy signal to discrete symbols, and therefore it is natural to apply similar techniques. Finally, we introduce some application examples of human motion and models ranging from simulated human control to humanoid robot motion synthesis.

Human motion mapped to a humanoid robot

Author  Katsu Yamane

Video ID : 765

This video shows an example of a humanoid robot controlled using human motion. The robot is equipped with a tracking controller and a balance controller.

Chapter 65 — Domestic Robotics

Erwin Prassler, Mario E. Munich, Paolo Pirjanian and Kazuhiro Kosuge

When the first edition of this book was published domestic robots were spoken of as a dream that was slowly becoming reality. At that time, in 2008, we looked back on more than twenty years of research and development in domestic robotics, especially in cleaning robotics. Although everybody expected cleaning to be the killer app for domestic robotics in the first half of these twenty years nothing big really happened. About ten years before the first edition of this book appeared, all of a sudden things started moving. Several small, but also some larger enterprises announced that they would soon launch domestic cleaning robots. The robotics community was anxiously awaiting these first cleaning robots and so were consumers. The big burst, however, was yet to come. The price tag of those cleaning robots was far beyond what people were willing to pay for a vacuum cleaner. It took another four years until, in 2002, a small and inexpensive device, which was not even called a cleaning robot, brought the first breakthrough: Roomba. Sales of the Roomba quickly passed the first million robots and increased rapidly. While for the first years after Roomba’s release, the big players remained on the sidelines, possibly to revise their own designs and, in particular their business models and price tags, some other small players followed quickly and came out with their own products. We reported about theses devices and their creators in the first edition. Since then the momentum in the field of domestics robotics has steadily increased. Nowadays most big appliance manufacturers have domestic cleaning robots in their portfolio. We are not only seeing more and more domestic cleaning robots and lawn mowers on the market, but we are also seeing new types of domestic robots, window cleaners, plant watering robots, tele-presence robots, domestic surveillance robots, and robotic sports devices. Some of these new types of domestic robots are still prototypes or concept studies. Others have already crossed the threshold to becoming commercial products.

For the second edition of this chapter, we have decided to not only enumerate the devices that have emerged and survived in the past five years, but also to take a look back at how it all began, contrasting this retrospection with the burst of progress in the past five years in domestic cleaning robotics. We will not describe and discuss in detail every single cleaning robot that has seen the light of the day, but select those that are representative for the evolution of the technology as well as the market. We will also reserve some space for new types of mobile domestic robots, which will be the success stories or failures for the next edition of this chapter. Further we will look into nonmobile domestic robots, also called smart appliances, and examine their fate. Last but not least, we will look at the recent developments in the area of intelligent homes that surround and, at times, also control the mobile domestic robots and smart appliances described in the preceding sections.

Winbot window-cleaning robot

Author  Erwin Prassler

Video ID : 736

Video features window--cleaning robot Winbot at CES 2015.

PhillieBot Robot throws out the first pitch at a Phillies game

Author  Erwin Prassler

Video ID : 748

PhillieBot, developed by University of Pennsylvania, throws out the first pitch at a Phillies' baseball game (alas, in the dirt).

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.

Active teaching

Author  Maya Cakmak, Andrea Thomaz

Video ID : 107

Active-teaching scenario where the Simon humanoid robot asks for help during or after teaching, verifying that its understanding of the task is correct. Reference: M. Cakmak, A.L. Thomaz: Designing robot learners that ask good questions, Proc. ACM/IEEE Int. Conf. Human-Robot Interaction (HRI), Boston (2012), pp. 17–24, URL: https://www.youtube.com/user/SimonTheSocialRobot .

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.

Combined mobility and manipulation - Operational space control of free-flying space robots

Author  Jeff Russakow, Stephen Rock

Video ID : 787

An environmental space is simulated in two dimensions using an air-bearing over a flat surface. The operational space-control framework enables the dynamically decoupled motion and force control of the object.

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.

Collaborative robots

Author  Vijay Kumar

Video ID : 700

UPenn, USC, and Georgia Tech have established a framework for deploying an adaptive system of heterogeneous robots for urban surveillance. The aerial robots generate maps that are used to design navigation controllers and plan missions for the team. Multiple robots establish a mobile, ad-hoc communication network which is aware of the radio-signal strength between nodes and can adapt to conditions to maintain connectivity. A team of aerial and ground robots is able to monitor a small village and search for and localize human targets by the color of uniforms, while ensuring that the information from the team is available to a remotely-located human operator.

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.

An assistive, decision-and-control architecture for force-sensitive, hand–arm systems driven by human–machine interfaces (MM3)

Author  Jörn Vogel, Sami Haddadin, John D. Simeral, Daniel Bacher , Beata Jarosiewicz, Leigh R. Hochberg, John P. Donoghue, Patrick van der Smagt

Video ID : 621

This video shows a 3-D reach and grasp experiment using the Braingate2 neural interface system. The robot is controlled through a multipriority Cartesian impedance controller and its behavior is extended with collision detection and reflex reaction. Furthermore, virtual workspaces are added to ensure safety. On top of this a decision-and-control architecture, which uses sensory information available from the robotic system to evaluate the current state of task execution, is employed. Available assistive skills of the robotic system are not actively helping in this task but they are used to evaluate task success.