Inverse kinematics solution for trajectory tracking using artificial neural networks for SCORBOT ER-4u

This paper presents the kinematic analysis of the SCORBOT-ER 4u robot arm using a Multi-Layered Feed-Forward (MLFF) Neural Network. The SCORBOT-ER 4u is a 5-DOF vertical articulated educational robot with revolute joints. The Denavit-Hartenberg and Geometrical methods are the forward kinematic algorithms used to generate data and train the neural network. The learning of forward-inverse mapping enables the inverse kinematic solution to be found. The algorithm is tested on hardware (SCORBOT-ER 4u) and reliable results are obtained. The modeling and simulations are done using MATLAB 8.0 software

Valenced Media Effects on Robot-Related Attitudes and Mental Models: A Parasocial Contact Approach

Despite rapid advancements in robotics, most people still only come into contact with robots via mass media. Consequently, robot-related attitudes are often discussed as the result of habituation and cultivation processes, as they unfold during repeated media exposure. In this paper, we introduce parasocial contact theory to this line of research— arguing that it better acknowledges interpersonal and intergroup dynamics found in modern human–robot interactions. Moreover, conceptualizing mediated robot encounters as parasocial contact integrates both qualitative and quantitative aspects into one comprehensive approach. A multi-method experiment offers empirical support for our arguments: Although many elements of participants’ beliefs and attitudes persisted through media exposures, valenced parasocial contact resulted in small but meaningful changes to mental models and desired social distance for humanoid robots

Mind(sets) over machine? The influence of implicit self-theories in human-robot interaction.

Implicit self-theory asserts that an individual’s underlying beliefs about whether self-attributes (e.g., personality and intelligence) are fixed (entity theory) or mutable (incremental theory) causally affect motivation and behavior—with the most profound effects emerging in situations that involve challenges and setbacks. In support of this notion, several lines of research suggest that these beliefs hold some influence over people’s perception and behavior in diverse domains such as education, brand acceptance, and financial decision-making, among others. It is, however, presently unknown whether implicit self-theories exert such influence on people’s experiences of social robots. To address this gap, this research tested, in a series of three studies, the proposition that implicit self-theories represent an important variable, that influences the manner in which one perceives and responds to social robots. Study 1 provided the first evidence that an individual’s implicit self-theory orientation influences their perception of emerging social robots developed for everyday use. In particular, those endorsing more of an entity theory expressed greater robot anxiety than those endorsing more of an incremental theory. This finding held even when controlling for a range of covariate influences. In addition, incremental theorists, compared to entity theorists responded more favorably to social robots in general. Study 2 built on and substantively extended the findings of Study 1 by examining the effects of implicit self-theories on people’s responses to a robot that praised them for ability (i.e., intelligence), or for effort (i.e., hard work), after completing a difficult task. Results revealed that entity theorists evaluated a robot that delivered ability praise as more likable and intelligent than one that delivered effort praise. However, incremental theorists were unaffected by either praise type and rated the robot favorably regardless of the praise it delivered. Study 3, expanded the findings of Studies 1 and 2 to investigate the impact of implicit self-theories on people’s responses to a robot that defeats human beings in a general knowledge quiz game. Results showed that incremental theorists, compared to entity theorists were more likely to indicate an interest in playing against the robot after imagining losing to it. Whereas entity theorists rated such robots as presenting more identity and realistic threats. Together, these studies extend and enrich the Human-Robot Interaction (HRI) literature by establishing implicit self-theories as an important and meaningful variable for which to advance the understanding of HRI today. In so doing, this research attempts to respond to the ever-increasing demand for research on the psychological variables that underlie how people perceive and interact with robots—which, in many ways, has special urgency given the inexorable rise of AI and robotics in the social domain of everyday experience. In consequence, findings may contribute to the design of new or improved social robots that can reflect or shape beliefs, and, hence, build a greater sense of identification and trust with the intended human user

Human-Machine Communication: Complete Volume. Volume 6

his is the complete volume of HMC Volume 6