Abstract-For humanoid robots to fully realize their biped potential in a three-dimensional world and step over, around or onto obstacles such as stairs, appropriate and efficient approaches to execution, planning and perception are required. To this end, we have accelerated a robust model-based threedimensional tracking system by programmable graphics hardware to operate online at frame-rate during locomotion of a humanoid robot. The tracker recovers the full 6 degree-offreedom pose of viewable objects relative to the robot. Leveraging the computational resources of the GPU for perception has enabled us to increase our tracker's robustness to the significant camera displacement and camera shake typically encountered during humanoid navigation. We have combined our approach with a footstep planner and a controller capable of adaptively adjusting the height of swing leg trajectories. The resulting integrated perception-planning-action system has allowed an HRP-2 humanoid robot to successfully and rapidly localize, approach and climb stairs, as well as to avoid obstacles during walking.

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The objective of this research was to develop an initial taxonomy that grouped similar secondary in-vehicle tasks based on driving-related performance measures. This type of taxonomy would be useful to system designers when developing in-vehicle tasks and to researchers. Research was conducted using 2 infotainment systems, 17 tasks, and 89 participants to develop and validate an initial taxonomy. The results indicate that the 17 tasks could be parsed into four distinct groups ranging from selecting an AM band to destination entry. The groupings are based on number of glances and task completion time, which provided the best separation between the groups and consistent results for both static and dynamic testing.

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