Recent advances in the field of humanoid robotics increase the complexity of the tasks that such robots can perform. This makes it increasingly difficult and inconvenient to program these tasks manually. Furthermore, humanoid robots, in contrast to industrial robots, should in the distant future behave within a social environment. Therefore, it must be possible to extend the robot's abilities in an easy and natural way. To address these requirements, this work investigates the topic of imitation learning of motor skills. The focus lies on providing a humanoid robot with the ability to learn new bimanual tasks through the observation of object trajectories. For this, an imitation learning framework is presented, which allows the robot to learn the important elements of an observed movement task by application of probabilistic encoding with Gaussian Mixture Models. The learned information is used to initialize an attractor-based movement generation algorithm that optimizes the reproduced movement towards the fulfillment of additional criteria, such as collision avoidance. Experiments performed with the humanoid robot ASIMO show that the proposed system is suitable for transferring information from a human demonstrator to the robot. These results provide a good starting point for more complex and interactive learning tasks.beverage from a bottle into a glass by observing a teacher demonstrating this task. This choice is arbitrary and the researched methods do not depend on this specific choice but are general. An overview of the whole imitation learning process is depicted in figure 1 and explained in detail within the upcoming sections.

The recognition of places that have already been visited is a fundamental requirement for a mobile robot. This particularly concerns the detection of loop closures while mapping environments as well as the global localization w.r.t. to a prior map. This paper introduces a novel solution to place recognition with 2D LIDAR scans. Existing approaches utilize descriptors covering the local appearance of discriminative features within a bag-of-words (BOW) framework accompanied with approximate geometric verification. Though limiting the set of potential matches their performance crucially drops for increasing number of scans making them less appropriate for large scale environments. We present Geometrical Landmark Relations (GLARE), which transform 2D laser scans into pose invariant histogram representations. Potential matches are found in sub-linear time using an efficient Approximate Nearest Neighbour (ANN) search. Experimental results obtained from publicly available datasets demonstrate that GLARE significantly outperforms state-of-the-art approaches in place recognition for large scale outdoor environments, while achieving similar results for indoor settings. Our Approach achieves recognition rates of 93% recall at 99% precision for a dataset covering a total path of about 6.5 km.

Honey bees (Apis mellifera L.) are eusocial insects and well known for their complex division of labor and associative learning capability 1,2 . The worker bees spend the first half of their life inside the dark hive, where they are nursing the larvae or building the regular hexagonal combs for food (e.g. pollen or nectar) and brood . Later in life, each single bee leaves the hive to forage for food. Then a bee has to learn to discriminate profitable food sources, memorize their location, and communicate it to its nest mates 7 . Bees use different floral signals like colors or odors 7,8 , but also tactile cues from the petal surface 9 to form multisensory memories of the food source. Under laboratory conditions, bees can be trained in an appetitive learning paradigm to discriminate tactile object features, such as edges or grooves with their antennae 10,11,12,13 . This learning paradigm is closely related to the classical olfactory conditioning of the proboscis extension response (PER) in harnessed bees 14 . The advantage of the tactile learning paradigm in the laboratory is the possibility of combining behavioral experiments on learning with various physiological measurements, including the analysis of the antennal movement pattern. Video LinkThe video component of this article can be found at http://www.jove.com/video/50179/ Protocol 1. Preparing the Bees 1. Nectar or Pollen foragers are caught in the field either from a sucrose feeder or directly from the hive entrance while returning from a foraging trip. Each single bee is captured into a glass vial that is closed with a foam plug and taken immediately into the laboratory for further handling. 2. In the laboratory, the captured bees are briefly cooled in the refrigerator at 4 °C until they show first signs of immobility. 3. Each single immobilized bee is mounted in a small metal tube with adhesive tape between head and thorax and over the abdomen. Care should be taken that the proboscis and antennae are freely movable. 4. Paint the compound eyes and ocelli of the fixed bee with white paint (e.g. solvent-free Tipp-Ex) to occlude vision. 5. Add a small drop of melted wax behind the head of the bee to fix it to the tape between head and thorax to prevent head movements during recordings. 6. Mark each single bee with a number on the tape for better identification and place the tube with the fixed bee into a humid atmosphere to prevent dehydration. 7. Feed each single bee for 5 sec with droplets of a 30% sucrose solution presented with a syringe and let all bees recover for 30 min before starting with the tactile conditioning protocol. Tactile Conditioning1. Before conditioning, each single bee has to be tested for the proboscis extension response (PER) to a 30% sucrose stimulus applied to the antennae. Thereby the tip of the proboscis has to cross a virtual line between the opened mandibles. Discard all bees that don't respond with a PER to the sucrose stimulus. 2. For tactile conditioning use a brass cube (e.g. 3 x 5 mm) with a smooth or an engraved pa...

This paper's intention is to present a new approach for decomposing motion trajectories. The proposed algorithm is based on nonnegative matrix factorization, which is applied to a grid like representation of the trajectories. From a set of training samples a number of basis primitives is generated. These basis primitives are applied to reconstruct an observed trajectory, and the reconstruction information can be used afterwards for classification. An extension of the reconstruction approach furthermore enables to predict the observed movement further into the future. The proposed algorithm goes beyond the standard methods for tracking, since it doesn't use an explicit motion model but is able to adapt to the observed situation. In experiments we used real movement data to evaluate several aspects of the proposed approach.

Context VR as an application to enhance well-being is sparsely researched in the elderly population. The aim of the pilot study was to analyze the effect of 360° videos of different categories on the state of mind of seniors in nursing facilities. Furthermore, for the implementation in everyday life, the usability of the system and the experience for seniors should be evaluated. Methods The VR experience was used as a supplement to existing care services in three facilities on eight subjects. Mood state was assessed using the Questionnaire for the Assessment of Happiness before and after the intervention. Demographic data and technology acceptance were collected beforehand. After the intervention, subjects were interviewed about confounding factors and side effects, and nursing home staff were interviewed about the usability of the system and the organizational concept of implementation. Results There was a positive effect on state of mind. Gender and spatial mobility turned out to be influencing factors. Categories containing people, animals and action achieved the highest increases in the state of mind. Interest in using technical devices correlated negatively with the change in mood state. None of the subjects found the VR goggles distracting or reported motion sickness. Very good usability was indicated by the employees. Conclusion A very high willingness to use this technology was found among nursing staff and residents. The tendencies of the positive effect of 360° videos on the state of mind, as well as differentiation based on the mentioned characteristics gender and spatial mobility, should be verified by a larger sample to empirically validate the use of this technology to increase the quality of life.