5 accepted papers
Robots can tackle human–robot interaction (HRI) tasks through inverse reinforcement learning (IRL). However, offline IRL agents' performance is upper-bounded by experts. Limited demonstration fails to provide an overall picture of the environment, especially in real-world applications. To further en
For human-robot interaction (HRI), it is difficult to hand-craft all the rules for robots owing to diverse situations. Therefore, inverse reinforcement learning (IRL) is a potential solution that helps transfer human knowledge about interactions to robots. However, the feasibility of practically usi
Recent developments in conversational technologies have attracted researchers to study their applications in recommending items through conversations. It is considered that physical robots, rather than virtual ones, are effective in situations in which robots talk about items near participants. Howe
For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to complex human behaviors. We introduce the Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits huma…
Locomotion on the rough and variable ground surfaces is crucial for robots to complete various tasks. Recent advancement in numerical computation allow such locomotive robots to manipulate in real environments using a model-based control framework. This approach is successful if the precise model it…