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Object exploration using vision and active touch

机译:使用视觉和主动触摸进行对象探索

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Achieving object exploration with passive vision and active touch has been under investigation for thirty years. We build upon recent progress in biomimetic active touch that combines perception via Bayesian evidence accumulation with controlling the tactile sensor using perceived stimulus location. Here, passive vision is combined with active touch by providing a visual prior for each perceptual decision, with the precision of this prior setting the relative contribution of each modality. The performance is examined on an edge following task using a tactile fingertip (the TacTip) mounted on a robot arm. We find that the quality of exploration is a U-shaped function of the relative contribution of vision and touch; moreover, multi-modal performance is more robust, completing the contour when touch alone fails. The overall system has several parallels with biological theories of perception, and thus plausibly represents a robot model of visuo-tactile exploration in humans.
机译:通过被动视觉和主动触摸来实现物体探索已经进行了三十年的研究。我们以仿生主动触摸技术的最新进展为基础,该技术将通过贝叶斯证据积累的感知与使用感知刺激位置控制触觉传感器相结合。在此,通过为每个感知决策提供视觉先验,将被动视觉与主动触摸相结合,该先验的精度设置每个模态的相对贡献。使用安装在机器人手臂上的可触摸指尖(TacTip),在任务执行后的边缘检查性能。我们发现探索的质量是视觉和触觉的相对贡献的U形函数。此外,多模式性能更强健,仅在触摸失败时即可完成轮廓。整个系统与感知的生物学理论具有数个相似之处,因此可以合理地代表人类视觉-触觉探索的机器人模型。

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