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We describe a method of generating new motions associatively from unfamiliar indications. The associative motion generation system is composed of two neural networks: nonlinear principal component analysis (NLPCA) and Jordan recurrent neural network (JRNN). First, the system learns the correspondence relationship between an indication and a motion using training data. Second, associative values are...
A knowledge-based approach to imitation learning of motion generation for humanoid robots and an imitative motion generation system based on motion knowledge learning and reuse are described. The system has three parts: recognizing, learning, and modifying parts. The first part recognizes an instructed motion distinguishing it from the motion knowledge database by the continuous hidden Markov model...
To improve face-to-face interaction with robots, we developed a model for generating interactive facial expressions by using a simple recurrent network (SRN). Conventional models for robot facial expression use predefined expressions, so only a limited number of expressions can be presented. This means that the expression may not match the interaction and that the person may find the expressions monotonous...
A knowledge-based approach to imitation learning of motion generation for humanoid robots and an imitative motion generation system based on motion knowledge learning and reuse are described. The system has three parts: recognizing, learning, and modifying parts. The first part recognizes an instructed motion distinguishing it from the motion knowledge database by the hidden Markov model. When the...
Recently, as the relationship between robot and human has become closer, humans demand that robots pose familiar human-like characteristics. For a robot to live and communicate with people, it requires its own personality or individuality. Changing the mood transition of robots can change the perceptions people have of their characteristics. We propose an emotion generation model that represents a...
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