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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
Published:
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Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
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
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.