Imitation Learning of Whole-Body Grasps

Abstract— A system is detailed here for using imitation learning to teach a robot to grasp objects using both hand and wholebody 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...

• Type: Conference
• Year: 2006
• Where: IROS
• Authors: Kaijen Hsiao, Tomás Lozano-Pérez
• Keywords: Demonstration Grasp Trajectories, IROS 2006, Promising Grasp Candidate, Robotics, Target Object