Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Multibody Dynamics Model of a Human Hand for Haptics Interaction

In this paper we propose a strategy for modelling a human hand for Haptics interaction. The strategy consists in a parallel computing architecture that calculates the dynamics of a hand; this is accomplished by computing the dynamics of each finger in a parallel manner. In this approach multiple threads (e.g. haptics thread, graphics thread, collision detection thread, etc.) run concurrently and therefore we developed a synchronization mechanism for data exchange. We describe in detail the elements of the developed software

Application of an Intuitive, Glove-type Remote Control with Haptic Feedback to Quadcopters

Although remote controllers for drones, based upon a classic two-joystick architecture, are unwieldy, they still see widespread use. As a replacement, we propose a remote control with a glove-based architecture that utilizes haptic feedback from the quadcopter. The proposed controller should be far more intuitive, making drone flight easier and more intuitive. Additionally, since the pilot will have one hand free, he or she can use maps, electronics, and other aids much more straightforwardly than with a two-handed controller. While our technology is designed for drones, it also could see further usage in a wide variety of civilian and military applications, from entertainment to industry. This glove-based architecture with haptic feedback might well become a staple of the future

A Framework to Illustrate Kinematic Behavior of Mechanisms by Haptic Feedback

The kinematic properties of mechanisms are well known by the researchers and teachers. The theory based on the study of Jacobian matrices allows us to explain, for example, the singular configuration. However, in many cases, the physical sense of such properties is difficult to explain to students. The aim of this article is to use haptic feedback to render to the user the signification of different kinematic indices. The framework uses a Phantom Omni and a serial and parallel mechanism with two degrees of freedom. The end-effector of both mechanisms can be moved either by classical mouse, or Phantom Omni with or without feedback

Beyond Gazing, Pointing, and Reaching: A Survey of Developmental Robotics

Developmental robotics is an emerging field located at the intersection of developmental psychology and robotics, that has lately attracted quite some attention. This paper gives a survey of a variety of research projects dealing with or inspired by developmental issues, and outlines possible future directions

Multimodal Hierarchical Dirichlet Process-based Active Perception

In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object. We propose an MHDP-based active perception method that uses the information gain (IG) maximization criterion and lazy greedy algorithm. We show that the IG maximization criterion is optimal in the sense that the criterion is equivalent to a minimization of the expected Kullback--Leibler divergence between a final recognition state and the recognition state after the next set of actions. However, a straightforward calculation of IG is practically impossible. Therefore, we derive an efficient Monte Carlo approximation method for IG by making use of a property of the MHDP. We also show that the IG has submodular and non-decreasing properties as a set function because of the structure of the graphical model of the MHDP. Therefore, the IG maximization problem is reduced to a submodular maximization problem. This means that greedy and lazy greedy algorithms are effective and have a theoretical justification for their performance. We conducted an experiment using an upper-torso humanoid robot and a second one using synthetic data. The experimental results show that the method enables the robot to select a set of actions that allow it to recognize target objects quickly and accurately. The results support our theoretical outcomes.Comment: submitte