Humanoid environmental perception with Gaussian process regression

Nowadays, humanoids are increasingly expected acting in the real world to complete some high-level tasks humanly and intelligently. However, this is a hard issue due to that the real world is always extremely complicated and full of miscellaneous variations. As a consequence, for a real-world-acting robot, precisely perceiving the environmental changes might be an essential premise. Unlike human being, humanoid robot usually turns out to be with much less sensors to get enough information from the real world, which further leads the environmental perception problem to be more challenging. Although it can be tackled by establishing direct sensory mappings or adopting probabilistic filtering methods, the nonlinearity and uncertainty caused by both the complexity of the environment and the high degree of freedom of the robots will result in tough modeling difficulties. In our study, with the Gaussian process regression framework, an alternative learning approach to address such a modeling problem is proposed...