Wooyoung Kwon

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For effective human-robot interaction, a robot should be able to make prediction about future circumstance. This enables the robot to generate preparative behaviors to reduce waiting time, thereby greatly improving the quality of the interaction. In this paper, we propose a novel probabilistic temporal prediction method for proactive interaction that is(More)
Reinforcement learning (RL) has been used as a learning mechanism for a mobile robot to learn state-action relations without a priori knowledge of working environment. However, most RL methods usually suffer from slow convergence to learn optimum state-action sequence. In this paper, it is intended to improve a learning speed by compounding an existing(More)
The two most important abilities for a robot to survive in a given environment are selecting and learning the most appropriate actions in a given situation. Historically, they have also been the biggest problems in robotics. To help solve this problem, we propose a two-layered action selection mechanism (ASM) which designates an action pattern layer and a(More)
For a robot to interact with a person effectively, it needs to predict future events that will be caused by the person to occur. By predicting events, a robot can take some preparative actions to reduce the waiting time and greatly improve the interaction. To select the best proactive actions and the best times for those actions, we propose a hybrid(More)