Humanoid environmental perception with Gaussian process regression

Dingsheng Luo

Abstract:

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 and discussed. Meanwhile, to debase the
influence derived from limited sensors, the idea of fusing multiple sensory information is also involved. To evaluate the
effectiveness, with two representative environment changing tasks, that is, suffering unknown external pushing and
suddenly encountering sloped terrains, the proposed approach is applied to a humanoid, which is only equipped with a
three-axis gyroscope and a three-axis accelerometer. Experimental results reveal that the proposed Gaussian process
regression-based approach is effective in coping with the nonlinearity and uncertainty of the humanoid environmental
perception problem. Further, a humanoid balancing controller is developed, which takes the output of the Gaussian
process regression-based environmental perception as the seed to activate the corresponding balancing strategy. Both
simulated and hardware experiments consistently show that our approach is valuable and leads to a good base for
achieving a successful balancing controller for humanoid.