J.hyeon Park

J.hyeon Park

Ph.D student, Research interest is deep learning in robotics (perception and action)

Posts by Collection

portfolio

publications

Bayesian Online Learning for Information-based Multi-Agent Exploration with Unknown Radio Signal Distribution

Published in International Federation Automatic Control (IFAC) World Congress, 2017

Authors: Lim, Jinhong and Park, J Hyeon and Kim, H Jin
Abstract: ‘Exploring an unknown environment with multiple robots is an enabling technology for many useful applications. This paper investigates decentralized motion planning for multi-agent exploration in a field with unknown received signal strength (RSS) distribution. The environment is modelled with a Gaussian process using Bayesian online learning by sharing the information obtained from the measurement history of each robot. Then we use the mean function of the Gaussian process to infer the multiple RSS source locations. The inferred source locations are modelled as the probability distribution using Gaussian mixture-probability hypothesis density (GM-PHD) filter. This modelling enables nonparametric approximation of mutual information between source locations and future robot positions. We combine the variance function of the Gaussian process and the mutual information to design an informative and noise-robust planning algorithm for multiple robots. The experimental performance is analyzed by comparing with the variance-based planning algorithm.’

Learning Transformable and Plannable se(3) Features for Scene Imitation of a Mobile Service Robot

Published in IEEE Robotics and Automation Letters (RA-L), 2020

Authors: ‘J. h. Park, J. Kim, Y. Jang, I. Jang and H. J. Kim’ Abstract: ‘Deep neural networks facilitate visuosensory inputs for robotic systems. However, the features encoded in a network without specific constraints have little physical meaning. In this research, we add constraints on the network so that the trained features are forced to represent the actual twist coordinates of interactive objects in a scene. The trained coordinates describe 6d-pose of the objects, and SE(3) transformation is applied to change the coordinate system. This algorithm is developed for a mobile service robot that imitates an object-oriented task by watching human demonstrations. As the robot has mobility, the video demonstrations are collected from the different view points. Our feature trajectories of twist coordinates are synthesized in the global coordinate after SE(3) transformation is applied according to robot localization. Then, the trajectories are trained as probabilistic model and imitated by the robot with geometric dynamics of se(3) . Our main contribution is to develop a trainable robot with visually demonstrated human performances. Additionally, our algorithmic contribution is to design a scene interpretation network where se(3) constraints are incorporated to estimate 6d-pose of objects.’

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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