Robotic grasping of unmodeled objects using time-of-flight range data and finger torque information

Robotic grasping in an open environment requires both object-specific as well as general grasping skills. When the objects are previously known it is possible to employ techniques that exploit object models, like geometrical grasping simulators. On the other hand, a competent system will also be able to deal with unmodeled objects using general solutions. In this paper we present an integrated system for autonomous rigid-object pick-up tasks in domestic environments, focusing on the gripping of unmodeled objects and exploiting sensor feedback from the robot hand to monitor the grasp. We describe the perception system based on time-of-flight range data, the grasp pose optimization algorithm and the grasp execution. The performance and robustness of the system is validated by experiments including pick-up tasks on many different common kitchen items.