To be a good helper, grasping and manipulation are the most important abilities of a service robot. It should be able to adapt its manipulation actions to new tasks and environments. During the execution, it is important to rate the success of actions, so that the robot can plan and execute further actions to correct and recover from the failed actions. The successful execution of manipulation actions depends on various factors during the whole execution, such as the position of the robotic hand and forces exerted by the robot. The goal of the manipulation action monitoring is to estimate the success state from the huge amount of data collected during the execution. The main challenge to solve this problem is to identify the success or failure state from the the high dimensional data collection. We propose a method to classify ongoing activities using a set of support vector machines (SVM). After a supervised training process with manually labeled successful or failure results, our system can correctly estimate the resulting state of a manipulation activity. We present experiments on our bimanual manipulation demonstrator and evaluate the results.

For complex manipulation with multiple objects a service robot needs information about the structure of its environment including how and where it can manipulate in it. For this purpose, we introduce the Graspability. It is a measure describing the quality of a pose in Cartesian space for grasping or placing an object. The graspability considers kinematic reachability for a grasping robot and available grasps for the object. It is based on the assumption, that in manipulation tasks, objects tend to be located on a planar surfaces and have to be graspable from that plane, based on that assumption, we develop a discrete map of the environment which enables the use of the graspability in an autonomous planning system for complex manipulation tasks with multiple objects. Generated manipulation actions are evaluated on a real robot.

An autonomous ice cream serving robot is presented in this video. The video was filmed during the Automatica 2010 trade fair in Munich. Within four days, approximately 250 scoops of different kinds of ice cream have been served to visitors during the fair. Using the KUKA light-weighted robotic arms and the DLR/HIT robotic hands, two scientific aspects are shown in this video: stable grasping of fragile objects and manipulation of cream-like mass using a time-of-flight camera. Impedance control are intensively used both by grasping and manipulation. The video shows an autonomous service robot scenario beyond simple fetch-and-carry chores.