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In this paper, we propose a method to achieve temporal scaling of leg motions as a fundamental technique for a music feedback system of a dancing humanoid robot. We asked dancers to perform dance motion at normal musical tempo and faster musical tempos and observed how dancers modified performance for given musical tempos. The obtained insights from the observation are 1) a dancer needs to preserve...
Interaction of a robot with dynamic environments would require continuous adaptation of force and impedance, which is generally not available in current robot systems. In contrast, humans learn novel task dynamics with appropriate force and impedance through the concurrent minimization of error and energy, and exhibit the ability to modify movement trajectory to comply with obstacles and minimize...
Learning control strategies from examples has been identified as an important capability for many robotic systems. In this work we show how the learning process can be aided by autonomously filtering the training set provided to improve key properties of the learning process. Demonstrated with data gathered for manipulation tasks, the results herein show the improved performance when autonomous filtering...
Both self-learning architecture (embedded structure) and explicit/implicit teaching from other agents (environmental design issue) are necessary not only for one behavior learning but more seriously for life-time behavior learning. This paper presents a method for a robot to understand unfamiliar behavior shown by others through the collaboration between behavior acquisition and recognition of observed...
Extracting, optimizing and transferring control skill is an important step towards creating an intelligent robot in a cooperative environment. Traditionally, control skill analysis focuses on how to design a state-action map to provide appropriate actions according to the states. Therefore approaches such as dynamic programming and neural networks are employed to compute the appropriate actions for...
Dynamic system-based motor primitives have enabled robots to learn complex tasks ranging from Tennis-swings to locomotion. However, to date there have been only few extensions which have incorporated perceptual coupling to variables of external focus, and, furthermore, these modifications have relied upon handcrafted solutions. Humans learn how to couple their movement primitives with external variables...
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