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The challenging problem of planning manipulation tasks for dexterous robotic hands can be significantly simplified if the robot system has the ability to learn manipulation skills by observing a human demonstrator. Toward this goal, we present a novel computer vision based hand posture recognition system to serve as an intelligent interface for skill transfer in robotic manipulation. We use the inner...
Transferring motion from a human demonstrator to a humanoid robot is an important step toward developing robots that are easily programmable and that can replicate or learn from observed human motion. The so called motion retargeting problem has been well studied and several off-line solutions exist based on optimization approaches that rely on pre-recorded human motion data collected from a marker-based...
This paper presents a model-based, Cartesian control theoretic approach for estimating human pose from features detected using depth images obtained from a time of flight imaging device. The features represent positions of anatomical landmarks, detected and tracked over time based on a probabilistic inferencing algorithm. The detected features are subsequently used as input to a constrained, closed...
Many advanced motion control strategies developed in robotics use captured human motion data as valuable source of examples to simplify the process of programming or learning complex robot motions. Direct and online control of robots from observed human motion has several inherent challenges. The most important may be the representation of the large number of mechanical degrees of freedom involved...
In this paper, we consider representations and formulations of systems of rigid bodies suitable for performance assessment, computer simulation and motion animation. In performance assessment, more realistic models of humanoids can be constructed. These models allow the integration of the information processing of the central nervous system in the car-humanoid model. Specifically, the effects of short-...
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