Humanoid Interaction Approach: Exploring Meaningful Order in Complex Interactions

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Available from: Yasuo Kuniyoshi, Aug 16, 2015
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    • "Other parts of the body are still under construction. For a detailed discussion of the whole system see [8] and [9]. Motor control and sensor processing is currently performed via a set of six PCs connected to our humanoid. "
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    ABSTRACT: Human interaction involves a number of factors. One key and noticeable factor is the mass perceptual problem. Humans are equipped with a large number of receptors, equipped for seeing, hearing and touching, to name just a few. These stimuli bombard us continuously, often not on a singular basis. Typically multiple stimuli are activated at once, and in responding to these stimuli, variations of responses are exhibited. The current aim of our project is to provide an architecture, that will enable a humanoid robot to yield meaningful responses to complex and continuous interactions, similar to that of humans. We present our humanoid, a system which is able to simultaneously detect the spatial orientation of a sound source, and is also able to detect and mimic the motion of the upper body of a person. The motion produced by our system is human like-ballistic motion. The focus of the paper is on how we have come about the integration of these components. A continuous interactive experiment is presented in demonstrating our initial effort. The demonstration is in the context of our humanoid interacting with a person. Through the use of spatial hearing and multiple visual cues, the system is able to track a person, while mimicking the persons upper body motion. The system has shown to be robust and tolerable to failure, in performing experiments for a long duration of time
    Robotics and Automation, 2000. Proceedings. ICRA '00. IEEE International Conference on; 02/2000
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    ABSTRACT: Recently, the field of humanoid robotics attracts more and more interest and the research on humanoid locomotion based on central pattern generators (CPG) reveals many challenging aspects. This paper describes the design of CPG for stable humanoid bipedal locomotion using an evolutionary approach. In this research, each joint of the humanoid is driven by a neuron that consists of two coupled neural oscillators, and corresponding joint's neurons are connected by strength weight. To achieve natural and robust walking pattern, an evolutionary-based multi-objective optimization algorithm is used to solve the weight optimization problem. The fitness functions are formulated based on zero moment point (ZMP), global attitude of the robot and the walking speed. In the algorithms, real value coding and tournament selection are applied, the crossover and mutation operators are chosen as heuristic crossover and boundary mutation respectively. Following evolving, the robot is able to walking in the given environment and a simulation shows the result
    Intelligent Robots and Systems, 2000. (IROS 2000). Proceedings. 2000 IEEE/RSJ International Conference on; 02/2000
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