arXiv subjects

Youngbin Park

Publications and source records attributed to Youngbin Park.

4 recordsLinked to original sources

arXiv2025

Conservation of transverse orbital angular momentum for spatiotemporal optical vortices

The transverse orbital angular momentum (OAM) of spatiotemporal optical vortices (STOVs) has been a topic of active debate in recent years. Previous studies, relying on narrowband and paraxial approximations, resulted in unprecedented conclusions. Some researchers have proposed that electromagnetic waves require a modified definition of OAM to ensure conservation. In this work, we demonstrate that the transverse OAM of STOVs is highly sensitive to these commonly adopted approximations. Using the standard definition of OAM for electromagnetic waves, we demonstrate that the total transverse OAM is conserved during propagation once the narrowband and paraxial assumptions are removed.

physics.optics↗

arXiv2025

Three-dimensional abruptly autofocusing by counter-propagating Airy pulses with radial Airy beam profile

We report the experimental observation of a three-dimensional abruptly autofocusing effect by synthesizing a radially distributed Airy beam with two counter-propagating Airy pulses in time. As the wave packet propagates in a dispersive medium, the radially distributed Airy beam converges inward to the center point. Two Airy pulses counter-propagate toward each other to merge to form a high peak power pulse. As the result, the high intensity emerges abruptly as the wave packet achieves three-dimensional focusing. This autofocusing effect is believed to have potential applications such as material modification, plasma physics, nanoparticle manipulations, etc.

physics.optics↗

arXiv2020

Acceleration of Actor-Critic Deep Reinforcement Learning for Visual Grasping in Clutter by State Representation Learning Based on Disentanglement of a Raw Input Image

For a robotic grasping task in which diverse unseen target objects exist in a cluttered environment, some deep learning-based methods have achieved state-of-the-art results using visual input directly. In contrast, actor-critic deep reinforcement learning (RL) methods typically perform very poorly when grasping diverse objects, especially when learning from raw images and sparse rewards. To make these RL techniques feasible for vision-based grasping tasks, we employ state representation learning (SRL), where we encode essential information first for subsequent use in RL. However, typical representation learning procedures are unsuitable for extracting pertinent information for learning the grasping skill, because the visual inputs for representation learning, where a robot attempts to grasp a target object in clutter, are extremely complex. We found that preprocessing based on the disentanglement of a raw input image is the key to effectively capturing a compact representation. This enables deep RL to learn robotic grasping skills from highly varied and diverse visual inputs. We demonstrate the effectiveness of this approach with varying levels of disentanglement in a realistic simulated environment.

cs.LG↗

arXiv2016

Tracking Human-like Natural Motion Using Deep Recurrent Neural Networks

Kinect skeleton tracker is able to achieve considerable human body tracking performance in convenient and a low-cost manner. However, The tracker often captures unnatural human poses such as discontinuous and vibrated motions when self-occlusions occur. A majority of approaches tackle this problem by using multiple Kinect sensors in a workspace. Combination of the measurements from different sensors is then conducted in Kalman filter framework or optimization problem is formulated for sensor fusion. However, these methods usually require heuristics to measure reliability of measurements observed from each Kinect sensor. In this paper, we developed a method to improve Kinect skeleton using single Kinect sensor, in which supervised learning technique was employed to correct unnatural tracking motions. Specifically, deep recurrent neural networks were used for improving joint positions and velocities of Kinect skeleton, and three methods were proposed to integrate the refined positions and velocities for further enhancement. Moreover, we suggested a novel measure to evaluate naturalness of captured motions. We evaluated the proposed approach by comparison with the ground truth obtained using a commercial optical maker-based motion capture system.

cs.CV↗