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Teaching robots manipulative skills through human demonstration is an important research problem and can be used to quickly program robots in future manufacturing industries. To understand human demonstration, manipulative actions need to be recognized. To improve the recognition performance, we use three kinds of sensors to capture the motion and force involved in the fine manipulative actions. In...
Human control strategy has been considered as a robust control method in mastering the complex and dynamic skill. In this paper, we present the problem of how human control strategy can be represented as a learning based approach and how a human strategy controller can be used in controlling dynamically stable while statically unstable, Wheeled Inverted Pendulum (WIP). The controller is designed by...
Acquiring robot assembly skills through human demonstration is a challenging problem. To achieve this goal, not only the actions and objects have to be shown to the robot, but also the effect of the action needs to be estimated. Recognizing the subtle assembly actions is a non-trivial task, and it is difficult to estimate the effect of the action on the assembly parts due to the small part sizes....
Recognition of human actions using a vision based approach is a challenging task. To improve the action recognition performance, we proposed a hierarchical probabilistic model based framework which not only models the dynamics of the actions but also considers contextual constraints in terms of object/action correlation and action sequential constraints. By considering the action/object correlation,...
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