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In this work, we propose an approach that relies on cues from depth perception from RGB-D images, where features related to human body motion (3D skeleton features) are used on multiple learning classifiers in order to recognize human activities on a benchmark dataset. A Dynamic Bayesian Mixture Model (DBMM) is designed to combine multiple classifier likelihoods into a single form, assigning weights...
For service robots operating in indoor environments, the crucial task of navigation is often complicated by the presence of people. Simply treating humans in the environment as additional (often moving) obstacles can violate the complex set of social rules by which people navigate around each other. In contrast, emulating human behavior and navigating in a socially appropriate manner could positively...
In this contribution, we describe an unified controller we implemented in order to simplify creating the common interactive behaviors that the robot may performs with human users. This controller is based on a multi-objective optimization technique allowing simultaneous achievement of different constraints and goals. The core of the developed algorithm is based on the particle swarm optimization technique...
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