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A robot companion should adapt to its user's needs by learning to perform new tasks. In this paper, we present a robot playmate that learns and adapts to tasks chosen by the child on a touchscreen tablet. We aim to solve the task learning problem using an experience-based learning framework that stores human demonstrations as task instances. These instances are retrieved when confronted with a similar...
One of the key elements for building a long-term robotic companion is incorporating the ability for a robot to continuously learn and engage in new tasks. Utilizing a defined workspace that provides various shared content between human and robot could assist in this learning process. Here, we propose integrating a touchscreen tablet and a robot learner for engaging the user during human-robot interaction...
With rapid developments in the manufacturing industry, there has been increasing attention on PLC training for implementing factory automation. In order to meet the increasing demand for PLC training, this paper proposes a PLC wiring training system using AR. The proposed training system is a hybrid learning system for technology education, dedicated to wiring practice, the input part of PLC. Cases...
We propose the use of a multifactor model that extends Grassmann manifold to multiple factor frameworks. Both manifold learning algorithms and multifactor analysis are state-of-the-art dimension reduction techniques that are suitable to model variations of face images. In this paper, we demonstrate that Grassmann manifold can be extended to Mul-tifactor Grassmann manifold when used in conjunction...
In this paper, we focus on robot intelligence to generate turn-taking strategies in response to human play actions. This work builds on our previous work on play behavior recognition, and expands it to the child-robot therapeutic domain where the robot must understand and learn the play of a child and take turns manipulating the toys. The main contribution of this work is a novel attempt in applying...
Lately, Multilinear Principal Component Analysis (MPCA) has been successfully applied to face recognition since MPCA provides analysis of multiple factors of face images such as people's identities, viewpoints, and lighting conditions. MPCA employees multiple linear subspaces constructed by varying factors. In this paper, we propose nonlinear submanifold analysis, which can represent the variation...
In this paper, we discuss a methodology to build a system for a robot playmate that extracts and sequences low-level play primitives during a robot-child interaction scenario. The motivation is to provide a robot with basic knowledge of how to manipulate toys in an equivalent manner as a human does - as a first step in engaging children in cooperative play. Our approach involves the extraction of...
T3D face reconstruction from a single 2D image is mathematically ill-posed. However, to solve ill-posed problems in the area of computer vision, a variety of methods has been proposed; some of the solutions are to estimate latent information or to apply model based approaches. In this paper, we propose a novel method to reconstruct a 3D face from a single 2D face image based on pose estimation and...
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