Jong Gwan Lim

Online touch behavior recognition of hard-cover robot using temporal decision tree classifier

Touch is obviously an important channel along with vision and speech for natural human robot Interaction. However, as most service robots are generally specialized for their own service, touch-centered shape design and additional costs/computation less related to their own tasks can represent a limit to the application of a touch system on a service robot. This paper originated from the motivation to apply a touch system with lower costs/computation to robots without design modifications. The proposed touch recognition system features hardware that is simply composed of charge-transfer touch sensor arrays, an accelerometer and a temporal decision tree classifier intended for online recognition and computational time reduction. Experiments performed by 12 people shows the practicability of the system. The results showed an average recognition rate of 83% with respect to the 4 touch patterns of hit, beat, rub and push.

A robust online touch pattern recognition for dynamic human-robot interaction

This paper presents a novel touch pattern recognition algorithm for dynamic proximate interaction between a robot and a human. At first, in order to guarantee reactive responses to various touch patterns, an online touch pattern algorithm is proposed based on a Temporal Decision Tree(TDT). Second, dynamic movements of a robot in a real interaction situation usually deteriorate the confidence level of the pattern classifier. A robust method to compensate for inconsistent recognition results in the dynamic interaction is proposed by a Consistency Index(CI), which estimates consistency degrees of human touch patterns over time. The algorithms are applied to a hard-cover touch recognition module, which is being developed for recognizing the four kinds of emotional touch patterns mainly used in human-robot affective interaction. The recognition performance is evaluated in a simple game scenario environment with KaMERo (KAIST Motion Expressive Robot), which is an emotionally interactive robot platform. The results show that the proposed algorithm guarantees commercially applicable recognition performance by compensating for the misclassification inherent in the dynamic movements of a robot.

Neural network based motion segmentation for accelerometer applications

Of several research issues related to motion interaction using inertia measurement units, faster motion segmentation without accuracy loss has recently been raised. Instead of using excessive filtering that produces time delay or tricky use of multiple thresholds that cause difficulty in parameter optimization, this poster demonstrates that time series prediction using neural networks significantly decreases time delay and guarantees rigid motion segmentation by detecting end points in accelerometer signals. According to a general pattern recognition procedure, feature selection is made by a filtering method and the optimal structure is determined by cross validation. Radial basis function networks and Multi-Layer Perceptrons (MLPs) are tested and the results are compared with the conventional methods to evaluate accuracy and time delay in a handwriting case in 3D space. This study confirms that MLP shows the best accuracy and shortens the time delay by 1/4∼1/3 compared to the conventional methods.

Bats' Auditory Pathway Modeling for Multiple Targets Localization

In this paper, the feasibility of multiple targets localization with a ultrasonic transmitter and only two ultrasonic sensors are tested and its results are given. For this purpose, an experimental hardware system and algorithm which models after bats vocalization and auditory pathway are built. To mimic bats behavior features, bats chirp composed of single band/full band signal component and frequency response in bats cochlear are modeled after. Time delay estimation is made by cross correlation and deconvolution between transmitting signal and its echoes. As a result, the combination of full band signal and filterbank shows the best performance and the reason is analyzed The physical and theoretical constraints of this approach for multiple targets localization are analyzed and some possible solutions for the improvement are proposed.