Abstract-The ability to recognize human actions is a fundamental problem in many areas of robotics research concerned with human-robot interaction or learning from human demonstration. In this paper, we present a new integrated approach to identifying and recognizing actions in human movement sequences and their reproduction in unknown situations. We propose a set of task-space features to construct probabilistic models of action classes. Based on this representation, we suggest a combined segmentation and classification algorithm which processes data non-greedily using an incremental lookahead to reliably locate transitions between actions. In a programming by demonstration scenario, our action models afford the generalization and reproduction of learned movements to previously unseen situations. To evaluate the performance of our approach, we consider typical manipulation tasks in a table top setting. In a sequence of human demonstrations, our approach successfully extracts and recognizes actions from different classes and subsequently generalizes them to unknown situations.

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In this paper we describe details of our winning team Nimb-Ro@Home at the RoboCup@Home competition 2012. This year we improved the gripper design of our robots and further advanced mobile manipulation capabilities such as object perception and manipulation planning. For human-robot interaction, we propose to complement faceto-face communication between user and robot with a remote user interface for handheld PCs. We report on the use of our approaches and the performance of our robots at RoboCup 2012.

Abstract. In this paper, we detail the contributions of our team NimbRo to the RoboCup @Home league in 2011. We explain design and rationale of our domestic service robot Cosero that we used for the first time in a competition in 2011. We demonstrated novel capabilities in the league such as real-time table-top segmentation, flexible grasp planning, and real-time tracking of objects. We also describe our approaches to humanrobot cooperative manipulation and 3D navigation. Finally, we report on the use of our approaches and the performance of our robots at RoboCup 2011.

Abstract-One of the major obstacles that hinders the application of robots to human day-to-day tasks is the current lack of flexible learning methods to endow the robots with the necessary skills and to allow them to adapt to new situations. In this work, we present a new intuitive method for teaching a robot anthropomorphic motion primitives. Our method combines the advantages of reinforcement and imitation learning in a single coherent framework. In contrast to existing approaches that use human demonstrations merely as an initialization for reinforcement learning, our method treats both ways of learning as homologous modules and chooses the most appropriate one in every situation.We apply Gaussian Process Regression to generalize a measure of value across the combined state-action-space. Based on the expected value, uncertainty, and expected deviation of generalized movements, our method decides whether to ask for a human demonstration or to improve its performance on its own, using reinforcement learning. The latter employs a probabilistic search strategy, based on expected deviation, that greatly accelerates learning while protecting the robot from unpredictable movements at the same time. To evaluate the performance of our approach, we conducted a series of experiments and successfully trained a robot to grasp an object at arbitrary positions on a table.