In this paper, we present an approach to imitation learning of arm movements in humanoid robots. Continuous hidden Markov models (HMMs) are used to generalize movements demonstrated to a robot multiple times. Characteristic features of the perceived movement, so-called key points, are detected in a preprocessing stage and used to train the HMMs. For the reproduction of a perceived movement, key points that are common to all (or almost all) demonstrations, so-called common key points, are used. These common key points are determined by comparing the HMM state sequences and selecting only those states that appear in every sequence. We also show how the HMM can be used to detect temporal dependencies between the two arms in dual-arm tasks. Experiments reported in this paper have been performed using a kinematics model of the human upper body to simulate the reproduction of arm movements and the generation of natural-looking joint configurations from perceived hand paths. Results are presented and discussed.

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Using projectors to create perspectively correct imagery on arbitrary display surfaces requires geometric knowledge of the display surface shape, the projector calibration, and the user's position in a common coordinate system. Prior solutions have most commonly modeled the display surface as a tessellated mesh derived from the 3D-point cloud acquired during system calibration.In this paper we describe a method for functional reconstruction of the display surface, which takes advantage of the knowledge that most interior display spaces (e.g. walls, floors, ceilings, building columns) are piecewise planar. Using a RANSAC algorithm to recursively fit planes to a 3D-point cloud sampling of the surface, followed by a conversion of the plane definitions into simple planar polygon descriptions, we are able to create a geometric model which is less complex than a dense tessellated mesh and offers a simple method for accurately modeling the corners of rooms. Planar models also eliminate subtle, but irritating, texture distortion often seen in tessellated mesh approximations to planar surfaces.

Projectors equipped with wide-angle lenses can have an advantage over traditional projectors in creating immersive display environments since they can be placed very close to the display surface to reduce user shadowing issues while still producing large images. However, wide-angle projectors exhibit severe image distortion requiring the image generator to correctively pre-distort the output image.In this paper, we describe a new technique based on Raskar's [14] two-pass rendering algorithm that is able to correct for both arbitrary display surface geometry and the extreme lens distortion caused by fisheye lenses. We further detail how the distortion correction algorithm can be implemented in a real-time shader program running on a commodity GPU to create low-cost, personal surround environments.