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A Layered Hidden Markov Model (LHMM) has been usually used for recognizing various human activities. In such a LHMM, the performance tends to be improved than that of a single layered HMM. To further enhance the performance of such a LHMM, in this paper, we propose a brain-inspired feedback mechanism. For this achievement, the LHMM is first modeled using a set of training data that the semantic information...
In Human-Robot Interaction, an intelligent robot should be able to learn social skills and reproduce such skills according to dynamic human's behaviors. To this end, both motion trajectories of a human and a robot are autonomously segmented, after which social skills are represented by combining hierarchical hidden Markov models and interaction dynamics (i.e., mass-spring-damper) to include three...
Manipulation tasks are characterized by continuous motion trajectories containing a set of key phases. In this paper, we propose a probabilistic method to autonomously segment the motion trajectories for estimating the key phases embedded in such a task. The autonomous segmentation process relies on principal component analysis to adaptively project into one of the low-dimensional subspaces, in which...
In this work, we propose methods for automatically generating primitive skills from the demonstration of a task. Additionally, we propose methods for improving existing primitive skills, and for automatically and incrementally adding new primitive skills. To validate our proposed methods, we present the experimental results of a human-like robot handling three gestures and a task of making coffee.
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