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Learning is a social and interactional endeavor, in which the learner generally receives support from his/her social environment. In this process, the learner's feedback is important as it provides information about the learner's current understanding which, in turn, enables the tutor to adjust his/her presentation accordingly. Thus, through their feedback learners can actively shape the tutor's presentation...
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing an improved form of the dynamic systems motor primitives originally introduced by Ijspeert et al. [2], we show how both discrete and rhythmic tasks can be...
Recently, with the advantages of peer tutoring, robot for education usage has been the new trend for new generation leaning technology. Robot is not only a toy, but also your learning partner. We considered integrating speech, position information and 3G cell phone to realize a highly-interactive learning platform.Teachers can design different scene with many stages for English or mathematic. Students...
For humanoid robots, the skill of gaze following is a foundational component in social interaction and imitation learning.We present a robotic system capable of learning the gaze following behavior in a real-world environment. First, the system learns to detect salient objects and to distinguish a caregiverpsilas head poses in a semi-autonomous manner. Then we present multiple scenes containing different...
Reinforcement learning is a useful tool for complex control problems that cannot be modeled mathematically nor solved theoretically. Direct policy search(DPS) is an approach for reinforcement learning that represents a policy using some model and searches an optimal parameter directly by optimization techniques such as genetic algorithms(GA). Instance-based policy is a policy representation model...
In this paper, we propose a model to develop robotspsila covert and overt behaviors by using reinforcement and supervised learning jointly. The covert behaviors are handled by a motivational system, which is achieved through reinforcement learning. The overt behaviors are directly selected by imposing supervised signals. Instead of dealing with problems in controlled environments with a low-dimensional...
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