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.

This chapter discusses how to determine the kinematic parameters and the inertial parameters of robot manipulators. Both instances of model identification are cast into a common framework of least-squares parameter estimation, and are shown to have common numerical issues relating to the identifiability of parameters, adequacy of the measurement sets, and numerical robustness. These discussions are generic to any parameter estimation problem, and can be applied in other contexts.

For kinematic calibration, the main aim is to identify the geometric Denavit–Hartenberg (DH) parameters, although joint-based parameters relating to the sensing and transmission elements can also be identified. Endpoint sensing or endpoint constraints can provide equivalent calibration equations. By casting all calibration methods as closed-loop calibration, the calibration index categorizes methods in terms of how many equations per pose are generated.

Inertial parameters may be estimated through the execution of a trajectory while sensing one or more components of force/torque at a joint. Load estimation of a handheld object is simplest because of full mobility and full wrist force-torque sensing. For link inertial parameter estimation, restricted mobility of links nearer the base as well as sensing only the joint torque means that not all inertial parameters can be identified. Those that can be identified are those that affect joint torque, although they may appear in complicated linear combinations.

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.

The field of rehabilitation robotics considers robotic systems that 1) provide therapy for persons seeking to recover their physical, social, communication, or cognitive function, and/or that 2) assist persons who have a chronic disability to accomplish activities of daily living. This chapter will discuss these two main domains and provide descriptions of the major achievements of the field over its short history and chart out the challenges to come. Specifically, after providing background information on demographics (Sect. 64.1.2) and history (Sect. 64.1.3) of the field, Sect. 64.2 describes physical therapy and exercise training robots, and Sect. 64.3 describes robotic aids for people with disabilities. Section 64.4 then presents recent advances in smart prostheses and orthoses that are related to rehabilitation robotics. Finally, Sect. 64.5 provides an overview of recent work in diagnosis and monitoring for rehabilitation as well as other health-care issues. The reader is referred to Chap. 73 for cognitive rehabilitation robotics and to Chap. 65 for robotic smart home technologies, which are often considered assistive technologies for persons with disabilities. At the conclusion of the present chapter, the reader will be familiar with the history of rehabilitation robotics and its primary accomplishments, and will understand the challenges the field may face in the future as it seeks to improve health care and the well being of persons with disabilities.

This chapter presents a discussion of modular robots from both an industrial and a research point of view. The chapter is divided into four sections, one focusing on existing reconfigurable modular manipulators typically in an industry setting (Sect. 22.2) and another focusing on self-reconfigurable modular robots typically in a research setting (Sect. 22.4). Both sections are sandwiched between the introduction and conclusion sections.

This chapter is focused on design issues. Rather than a survey of existing systems, it presents some of the existing systems in the context of a discussion of the issues and elements in industrial modular robotics and modular robotics research. The reader is encouraged to look at the references for further discussion on any of the presented topics.

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.

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.

The growth of medical robotics since the mid- 1980s has been striking. From a few initial efforts in stereotactic brain surgery, orthopaedics, endoscopic surgery, microsurgery, and other areas, the field has expanded to include commercially marketed, clinically deployed systems, and a robust and exponentially expanding research community. This chapter will discuss some major themes and illustrate them with examples from current and past research. Further reading providing a more comprehensive review of this rapidly expanding field is suggested in Sect. 63.4.

Medical robotsmay be classified in many ways: by manipulator design (e.g., kinematics, actuation); by level of autonomy (e.g., preprogrammed versus teleoperation versus constrained cooperative control), by targeted anatomy or technique (e.g., cardiac, intravascular, percutaneous, laparoscopic, microsurgical); or intended operating environment (e.g., in-scanner, conventional operating room). In this chapter, we have chosen to focus on the role of medical robots within the context of larger computer-integrated systems including presurgical planning, intraoperative execution, and postoperative assessment and follow-up.

First, we introduce basic concepts of computerintegrated surgery, discuss critical factors affecting the eventual deployment and acceptance of medical robots, and introduce the basic system paradigms of surgical computer-assisted planning, execution, monitoring, and assessment (surgical CAD/CAM) and surgical assistance. In subsequent sections, we provide an overview of the technology ofmedical robot systems and discuss examples of our basic system paradigms, with brief additional discussion topics of remote telesurgery and robotic surgical simulators. We conclude with some thoughts on future research directions and provide suggested further reading.