Imitation learning of whole-body grasps

A system is detailed here for using imitation learning to teach a robot to grasp objects using both hand and whole-body grasps, which use the arms and torso as well as hands. Demonstration grasp trajectories are created by teleoperating a simulated robot to pick up simulated objects, modeled as combinations of up to three aligned primitives-boxes, cylinders, and spheres. When presented with a target object, the system compares it against the objects in a stored database to pick a demonstrated grasp used on a similar object. By considering the target object to be a transformed version of the demonstration object, contact points are mapped from one object to the other. The most promising grasp candidate is chosen with the aid of a grasp quality metric. To test the success of the chosen grasp, a collision-free grasp trajectory is found and an attempt is made to execute it in simulation. The implemented system successfully picks up 92 out of 100 randomly generated test objects in simulation.