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Repetitive industrial tasks can be easily performed by traditional robotic systems. However, many other works require cognitive knowledge that only humans can provide. Human-Robot Collaboration (HRC) emerges as an ideal concept of co-working between a human operator and a robot, representing one of the most significant subjects for human-life improvement.The ultimate goal is to achieve physical interaction, where handing over an object plays a crucial role for an effective task accomplishment. Considerable research work had been developed in this particular field in recent years, where several solutions were already proposed. Nonetheless, some particular issues regarding Human-Robot Collaboration still hold an open path to truly important research improvements. This paper provides a literature overview, defining the HRC concept, enumerating the distinct human-robot communication channels, and discussing the physical interaction that this collaboration entails. Moreover, future challenges for a natural and intuitive collaboration are exposed: the machine must behave like a human especially in the pre-grasping/grasping phases and the handover procedure should be fluent and bidirectional, for an articulated function development. These are the focus of the near future investigation aiming to shed light on the complex combination of predictive and reactive control mechanisms promoting coordination and understanding. Following recent progress in artificial intelligence, learning exploration stand as the key element to allow the generation of coordinated actions and their shaping by experience.

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This article describes methods and strategies used to develop a humanoid robot with a distributed architecture approach where centralized and local control co-exist and concur to provide robust full monitoring and ef2cient control of a complex system with 22 DOF. A description of the hardware is given before introducing the architecture, since that greatly influences the methods implemented for the control systems and helps in understanding the general decisions. The platform is still undergoing improvement, but the results are very promising, mainly because many potential approaches and research issues have presented themselves and will provide opportunities to test distributed control systems with possibilities that go far beyond the classical control of robots. Some practical issues of servomotor control are also considered since that turned out to be necessary before implementing higher levels of control1 these are, in turn, addressed in the last part the article, which gives an example to demonstrate the possibility of keeping a humanoid robot in an upright balanced position using only local control after reaction forces on the ground.

Presently a vast variety of robust robotic arms are available commercially, some of which are extremely reliable in precision and repeatability. This makes them an ideal tool for research focused on manipulation. However, there is a lack of an easily accessible comparative analysis that can assist researchers in choosing an arm that fits their research objectives. With an objective to provide such an analysis, this paper provides a comparative survey of the state of the art in commercial robotic arms -classified based on their price, performance and suitability for research on manipulation. These arms are categorized into four classes: cheap educational arms, low price industrial arms, research oriented arms and modular light weight arms. Within each classification, some typical robotic arms are analyzed in detail to provide an overview of the functional capacities of the arms in that particular class.

This chapter is focused on recent advances in electroencephalogram (EEG) signal processing for brain computer interface (BCI) design. A general overview of BCI technologies is first presented, and then the protocol for motor imagery noninvasive BCI for mobile robot control is discussed. Our ongoing research on noninvasive BCI design based not on recorded EEG but on the brain sources that originated the EEG signal is also introduced. We propose a solution to EEG-based brain source recovering by combining two techniques, a sequential Monte Carlo method for source localization and spatial filtering by beamforming for the respective source signal estimation. The EEG inverse problem is previously studded assuming that the source localization is known. In this work for the first time the problem of inverse modeling is solved simultaneously with the problem of the respective source space localization.

Abstract. Exploring the full potential of humanoid robots requires their ability to learn, generalize and reproduce complex tasks that will be faced in dynamic environments. In recent years, significant attention has been devoted to recovering kinematic information from the human motion using a motion capture system. This paper demonstrates and evaluates the use of a Kinectbased capture system that estimates the 3D human poses and converts them into gestures imitation in a robot. The main objectives are twofold: (1) to improve the initially estimated poses through a correction method based on constraint optimization, and (2) to present a method for computing the joint angles for the upper limbs corresponding to motion data from a human demonstrator. The feasibility of the approach is demonstrated by experimental results showing the upper-limb imitation of human actions by a robot model.