We present an integrated grasping system for a mobile manipulator to grasp an unknown object of interest (OI) in an unknown environment. The system autonomously scans its environment, models the OI, plans and executes a grasp, while taking into account base pose uncertainty. Due to inherent line of sight limitations in sensing, a single scan of the OI often does not reveal enough information to complete grasp analysis; as a result, our system autonomously builds a model of an object via multiple scans from different locations until a grasp can be performed. A volumetric next-best-view (NBV) algorithm is used to model an arbitrary object and terminates modeling when force closure for the gripper is satisfied. Two experiments are presented: i) modeling and registration error is reduced by selecting viewpoints with more scan overlap, and ii) model reconstruction and grasps are successfully achieved while experiencing base pose uncertainty.

During a robot to human object handover task, several intended or unintended events may occur with the object - it may be pulled, pushed, bumped or simply held - by the human receiver. We show that it is possible to differentiate between these events solely via tactile sensors. Training data from tactile sensors were recorded during interaction of human subjects with the object held by a 3-finger robotic hand. A Bag of Words approach was used to automatically extract effective features from the tactile data. A Support Vector Machine was used to distinguish between the four events with over 95 percent average accuracy.