Musculoskelet al robots that are based on pneumatic actuation have a variety of properties, such as compliance and back-drivability, that render them particularly appealing for human-robot collaboration. However, programming interactive and responsive behaviors for such systems is extremely challenging due to the nonlinearity and uncertainty inherent to their control. In this paper, we propose an approach for learning Bayesian Interaction Primitives for musculoskelet al robots given a limited set of example demonstrations. We show that this approach is capable of real-time state estimation and response generation for interaction with a robot for which no analytical model exists. Human-robot interaction experiments on a 'handshake' task show that the approach generalizes to new positions, interaction partners, and movement velocities.
 The application of a robot to rehabilitation has become a matter of great concern. This paper deals with functional recovery therapy, one important aspect of physical rehabilitation. Single-joint therapy machines have already been achieved. However, for more efficient therapy, multjoint robots are necessary to achieve more realistic motion patterns. This kind of robot must have a high level of safety for humans. A pneumatic actuator may be available for such a robot, because of the compliance of compressed air. A pneumatic rubber artificial muscle manipulator has been applied to construct a therapy robot with two degrees of freedom (DOF). Also, an impedance control strategy is employed to realize various motion modes for the physical therapy modes. Further, for efficient rehabilitation, it is desirable to comprehend the physical condition of the patient. Thus, the mechanical impedance of the human arm is used as an objective evaluation of recovery, and an estimation method is proposed. Experiments show the suitability of the proposed rehabilitation robot system. Also referred to as learning by imitation, tutelage, or apprenticeship learning, Programming by Demonstration (PbD) develops methods by which new skills can be transmitted to a robot. This book examines methods by which robots learn new skills through human guidance. Taking a practical perspective, it covers a broad range of applications, including service robots. The text addresses the challenges involved in investigating methods by which PbD is used to provide robots with a generic and adaptive model of control. Drawing on findings from robot control, human-robot interaction, applied machine learning, artificial intelligence, and developmental and cognitive psychology, the book contains a large set of didactic and illustrative examples. Practical and comprehensive machine learning source codes are available on the books companion website: http: www.programming-by-demonstration.org   Pneumatic muscles are interesting in their use as actuators in robotics, since they have a high power weight ratio, a high-tension force and a long durability. This paper presents a two-axis planar articulated robot, which is driven by four pneumatic muscles. Every actuator is supplied by one electronic servo valve in 3 3-way function. Part of this work is the derivation of the model description, which describes a high nonlinear dynamic behavior of the robot. Main focus is the physical model for the pneumatic muscle and a detailed model description for the servo valves. The aim is to control the tool center point (TCP) of the manipulator, which bases here on a fast subsidiary torque regulator of the drive system compensating the nonlinear effects. As the robot represents a MIMO system, a second control objective is defined, which corresponds here to the average pressure of each muscle-pair. An optimisation-strategy is presented to meet the maximum stiffness of the controlled drive system. As the torque controller assures a fast linear input output behavior, a standardized controller is implemented which bases here on the Computed Torque Method to track the TCP. Measurement results show the efficiency of the presented cascaded control concept. ABSTRACTThis paper describes the construction and design decisions of an anthropomorphic musculoskelet al robot arm actuated by pneumatic artificial muscles. This robot was designed to allow human-inspired compliant movements without the need to replicate the human body-structure in detail. This resulted in an mechanically simple design while preserving the motoric characteristics of a human. Besides the constructional details of the robot we will present two experiments to show the robots abilities regarding to its dexterity and compliance.  This paper reports mechanical testing and modeling results for the McKibben artificial muscle pneumatic actuator. This device first developed in the 1950's, contains an expanding tube surrounded by braided cords. The authors report static and dynamic length-tension testing results and derive a linearized model of these properties for three different models. The results are briefly compared with human muscle properties to evaluate the suitability of McKibben actuators for human muscle emulation in biologically based robot arms. >