A hierarchical inverse model based on proprioception and DNN for robot reaching

Robot reaching ability serves as one of essential basis for many other manipulation skills, such as grasping, placing etc., and has been widely focused for decades. Inverse model, which plays a fundamental role within robot reaching ability, aims to produce motor commands to drive the system to the desired state. However, since the inverse model is always an one-to-many mapping, it suffers from the multi-solution issue and the adaptation problem for changing circumstances when traditional kinematic/dynamic models are employed. And thus, learning based approaches are investigated, including the deep learning based models. In this paper, to further improve the performance of deep neural networks (DNN) methods, a novel model is proposed, where both the proprioception and a hierarchical structure are involved. Here, the employed concept of proprioception is aimed to follow human mechanism, while the hierarchical structure is expected to mimic the fact that different joints usually play different effects in a manipulation process. Experiments are performed with respect to PKU-HR6.0 II humanoid robot, and the results illustrate the effectiveness and superiority of the proposed model.