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Robust dynamic gesture recognition algorithm is of great value for kinds of intelligent interactive systems. Most current researches on this field are based on trajectory time-series, which is unstable and limited. In this paper, we proposed a novel method to realize dynamic gesture recognition by analyzing the static trajectory images with Convolutional Neural Networks (CNN). First of all, a new...
Hidden Markov Models (HMMs) are applied to interoceptive data (in this case the sense of rotation by way of a gyroscope) acquired by a moving wheeled robot when contouring an indoor environment. We demonstrate the soundness of HMMs to solve the problem of robot localization in a topological model of the environment, particularly the kidnapped robot problem and position tracking. In this approach,...
Robotic devices are increasingly penetrating the human work spaces as stand alone units and helpers. It is believed that a robot could be easily integrated with humans, if the robot can learn how to behave in a socially acceptable manner. This involves a robot to observe, learn and comply with basic rules of human behaviors. As an example, one would expect a robot to travel in an environment without...
Action representation is a key issue in imitation learning for humanoids. With the recent finding of mirror neurons there has been a growing interest in expressing actions as a combination meaningful subparts called primitives. Primitives could be thought of as an alphabet for the human actions. In this paper we observe that human actions and objects can be seen as being intertwined: we can interpret...
In this paper, we introduce a hand-gesture-based control interface for navigating a car-robot. A 3-axis accelerometer is adopted to record a user's hand trajectories. The trajectory data is transmitted wirelessly via an RF module to a computer. The received trajectories are then classified to one of six control commands for navigating a car-robot. The classifier adopts the dynamic time warping (DTW)...
Due to the anticipated future, extensive use of robots, human beings will probably share common spaces with them. The relationships between robots and humans will be conducted at close distances. Predicting people's future positions helps robots understand human behavior and react safely and naturally. In this paper, we propose a method for predicting people's positions in crossing behaviors, i.e...
Human robot interaction is an emerging area of research with many challenges. Knowledge about human behaviors could lead to more effective and efficient interactions of a robot in populated environments. This paper presents a probabilistic framework for the learning and representation of human motion patterns in an office environment. It is based on the observation that most human trajectories are...
This paper provides a new method for modeling, clustering, and generalizing complex pseudo-periodic motions in a Robot Programming by Demonstration (PbD) framework. Relevant features of the trajectories are extracted by applying a linear mapping off the surface part using Moving Window Principal Component Analysis. A Hidden Markov Model is used for segmentation and temporal clustering of feature data...
Human robot interaction has attracted significant attention over the last couple of years. An important aspect of such robotic systems is to share the working space with humans and carry out the tasks in a socially acceptable way. In this paper, we address the problem of fusing socially acceptable behaviours into robot path planning. By observing an environment for a while, the robot learns human...
We consider the problem of learning robust models of robot motion through demonstration. An approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) is proposed to extract redundancies across multiple demonstrations, and build a time-independent model of a set of movements demonstrated by a human user. Two experiments are presented to validate the method, that consist of learning...
The paper presents a navigation algorithm for dynamic probabilistic environments. The static environment is unknown; moving pedestrians are detected and tracked on-line. Pedestrians are supposed to move along typical motion patterns represented by HMMs. The planning algorithm is based on an extension of the rapidly-exploring random tree algorithm, where the likelihood of the obstacles future trajectory...
A novel trajectory segmentation and modeling approach is presented. Trajectory segmentation and matching is an important step in the programming by demonstration (PbD) process to extract the user's intentions from multiple trajectories. To match multiple trajectories, the segmentation and modeling approach must be consistent and robust to disparities caused by robot dynamics and human imperfections...
In this paper, we proposed a method to estimate the destination of walking persons from their walking patterns, for avoidance of collision accidents between pedestrians and robots in a human-robot coexistence environment. We adopted the hidden Markov model (HMM) as a model to represent walking patterns. We constructed a model for each movement pattern. A movement pattern was defined with a departure...
This paper presents a method to recognize and generate sequential motions for object manipulation such as placing one object on another or rotating it. Motions are learned using reference-point-dependent probabilistic models, which are then transformed to the same coordinate system and combined for motion recognition/generation. We conducted physical experiments in which a user demonstrated the manipulation...
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