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Growing interest in biodiversity and conservation has increased the demand for accurate and consistent identification of arthropods. Unfortunately, professional taxonomists are already overburdened and underfunded and their numbers are not increasing with significant speed to meet the demand. In an effort to bridge the gap between professional taxonomists and non-specialists by making the results of taxonomic research more accessible, we present a partially automated pattern recognition system utilizing artificial neural networks (ANNs). Various artificial neural networks were trained to identify spider species using only digital images of female genitalia, from which key shape information had been extracted by wavelet transform. Three different sized networks were evaluated based on their ability to discriminate a test set of six species to either the genus or the species level. The species represented three genera of the wolf spiders (Araneae: Lycosidae). The largest network achieved the highest accuracy, identifying specimens to the correct genus 100% of the time and to the correct species an average of 81% of the time. In addition, the networks were most accurate when identifying specimens in a hierarchical system, first to genus and then to species. This test system was surprisingly accurate considering the small size of our training set.

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In this paper we propose and evaluate a control system to 1) learn and 2) adapt robot motion for continuous non-rigid contact with the environment. We present the approach in the context of wiping surfaces with robots. Our approach is based on learning by demonstration. First an initial periodic motion, covering the essence of the wiping task, is transferred from a human to a robot. The system extracts and learns one period of motion. Once the user/demonstrator is content with the motion, the robot seeks and establishes contact with a given surface, maintaining a predefined force of contact through force feedback. The shape of the surface is encoded for the complete period of motion, but the robot can adapt to a different surface, perturbations or obstacles. The novelty stems from the fact that the feedforward component is learned and encoded in a dynamic movement primitive.By using the feedforward component, the feedback component is greatly reduced if not completely canceled. Finally, if the user is not satisfied with the periodic pattern, he/she can change parts of motion through predefined gestures or through physical contact in a manner of a tutor or a coach.The complete system thus allows not only a transfer of motion, but a transfer of motion with matching correspondences, i. e. wiping motion is constrained to maintain physical contact with the surface to be wiped. The interface for both learning and adaptation is simple and intuitive and allows for fast and reliable knowledge transfer to the robot.Simulated and real world results in the application domain of wiping a surface are presented on three different robotic platforms. Results of the three robotic platforms, namely a 7 degree-of-freedom Kuka LWR-4 robot, the ARMAR-IIIa humanoid platform and the Sarcos CB-i humanoid robot, depict different methods of adaptation to the environment and coaching.

We present an extended version of our work on the design and implementation of a reference model of the human body, the Master Motor Map (MMM) which should serve as a unifying framework for capturing human motions, their representation in standard data structures and formats as well as their reproduction on humanoid robots. The MMM combines the definition of a comprehensive kinematics and dynamics model of the human body with 104 DoF including hands and feet with procedures and tools for unified capturing of human motions. We present online motion converters for the mapping of human and object motions to the MMM model while taking into account subject specific anthropometric data as well as for the mapping of MMM motion to a target robot kinematics. Experimental evaluation of the approach performed on VICON motion recordings demonstrate the benefits of the MMM as an important step towards standardized human motion representation and mapping to humanoid robots.

In this work, we present an integrated planner for collision-free single and dual arm grasping motions. The proposed Grasp-RRT planner combines the three main tasks needed for grasping an object: finding a feasible grasp, solving the inverse kinematics and searching a collision-free trajectory that brings the hand to the grasping pose. Therefore, RRTbased algorithms are used to build a tree of reachable and collision-free configurations. During RRT-generation, potential grasping positions are generated and approach movements toward them are computed. The quality of reachable grasping poses is scored with an online grasp quality measurement module which is based on the computation of applied forces in order to diminish the net torque. We also present an extension to a dual arm planner which generates bimanual grasps together with corresponding dual arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III.

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