IEEE Robotics and Automation Letters (RA-L) 2020: Learning Transformable and Plannable se(3) Features for Scene Imitation of a Mobile Service Robot

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...

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019: Fast and Safe Policy Adaptation via Alignment-based Transfer

Applying deep reinforcement learning to physical systems, as opposed to learning in simulation, presents additional challenges in terms of sample efficiency and safety. Collecting large amounts of hardware demonstration data is time-consuming and the exploratory behavior of reinforcement learning algorithms may lead the system into dangerous states, especially during the early stages of training. To address these challenges, we apply transfer learning to reuse a previously learned policy inst...

Journal of the Korean Astronomical Society (JKAS) 2014: Misclassified type 1 AGNs in the local universe

We search for misclassified type 1 AGNs among type 2 AGNs identified with emission line flux ratios, and investigate the properties of the sample. Using 4,113 local type 2 AGNs at 0.02<z<0.05 selected from Sloan Digital Sky Survey Data Release 7, we detected a broad component of the Ha line with a Full-Width at Half-Maximum (FWHM) ranging from 1,700 to 19,090 km/s for 142 objects, based on the spectral decomposition and visual inspection. The fraction of the misclassified type 1 AGNs am...