Natural object manipulation using anthropomorphic robotic hand through deep reinforcement learning and deep grasping probability network

Human hands can perform complex manipulation of various objects. It is beneficial if anthropomorphic robotic hands can manipulate objects like human hands. However, it is still a challenge due to the high dimensionality and a lack of machine intelligence. In this work, we propose a novel framework based on Deep Reinforcement Learning (DRL) with Deep Grasping Probability Network (DGPN) to grasp and relocate various objects with an anthropomorphic robotic hand much like a human hand. DGPN is used to predict the probability of successful human-like natural grasping based on the priors of human grasping hand poses and object touch areas. Thus, our DRL with DGPN rewards natural grasping hand poses according to object geometry for successful human-like manipulation of objects. The proposed DRL with DGPN is evaluated by grasping and relocating five objects including apple, light bulb, cup, bottle, and can. The performance of our DRL with DGPN is compared with the standard DRL without DGPN. The results show that the standard DRL only achieves an average success rate of 22.60%, whereas our DRL with DGPN achieves 89.40% for the grasping and relocation tasks of the objects.