Grasp space exploration method for an underactuated gripper using human initiated primitive grasps

Grasp planning and most specifically the grasp space exploration when considering adaptive and underactuated multifingered grippers is still an open issue in robotics. This article describes an efficient procedure for exploring the grasp space of such grippers that aims at generating reliable grasps given a known object pose. This article also assesses its performances, and compares it to more commonly used grasp space exploration methods. This method relies on a limited dataset of human specified expert grasps, and uses variational autoencoders to learn grasp intrinsic features together with an analytic grasp quality metric in a compact way from a computational point of view. It is evaluated both in simulation and on a real setup for the specific and complex case of adaptive and underactuated multifingered grasping. Using the proposed grasp planner, it reaches a grasp success rate of 99.54% on 7000 simulated trials, and successfully plans stable and reliable grasps on the real setup, with no failed grasp reported on around 30 trials. It also shows a significantly higher grasp success rate than other grasps space exploration methods.