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

7 citations · 7 across the 1 of their papers we have counts for

collaborators

2 papers

cs.LG2020★ 7 cited

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

Taewon Kim, Yeseong Park, Youngbin Park +1

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 v…

cs.CV2016

Tracking Human-like Natural Motion Using Deep Recurrent Neural Networks

Youngbin Park, Sungphill Moon, Il Hong Suh

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 pos…