Learning task transition from standing-up to walking for a squatted bipedal humanoid robot

Robots can perform a number of complex tasks, some of which need to be executed consecutively in an effective way to form a smooth behavior. In this case, task transition technique is involved. Due to that there may exist large differences between two successive tasks, how to switch to the target task from current robot status smoothly and efficiently becomes an important problem, especially for those tasks that are with some strict constraints or necessary requirements, e.g. stability for a walking task. Unlike previous approaches such as task weighting and blending, priority handling, kinematic control, interpolation, etc., in this research, a learning paradigm based approach is proposed. With respect to the requirements of efficiency and smoothness, the task transition problem is formulated as a typical machine learning issue, where those constraints are taken as factors of an objective function the learning process based on. Through a specific task transition problem, which is from standing-up to walking for a squatted bipedal humanoid robot, our approach is demonstrated and evaluated. Experimental results on both simulated and hardware humanoid robot PKU-HR5.1 verify that the proposed approach is effective.