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Generating Diverse Behaviors of Evolutionary Robots with Speciation for Theory of Mind

机译:借助思维理论生成进化机器人的多种行为

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Theory of Mind (ToM) is the ability to read another person's mind. To apply ToM in robots, robot should read the intention from target. However, it is difficult to read target's intention directly. Robot uses the sensors to measure distance from target because distance is the feature to read target's intention. Neural network has been widely used to control the robot for generating a diverse speciation. It has been less explored in behavior-based robotics. Speciation usually relies on a distance measure that allows different from the robot to target to be compared. In this paper, we proposed novel measure to generate diverse behaviors of a robot with speciation for ToM. It includes some distance measure such as Euclidean distance, cosine distance, arctangent distance, and edit distance. It generates diverse behaviors of the robot by neural network for ToM. The proposed method has been experimented on a real e-puck robot platform.
机译:心灵理论(ToM)是一种读取他人思想的能力。要在机器人中应用ToM,机器人应从目标读取意图。但是,难以直接读取目标的意图。机器人使用传感器来测量到目标的距离,因为距离是读取目标意图的特征。神经网络已被广泛用于控制机器人以生成各种物种。在基于行为的机器人技术中,它的研究较少。形态通常依赖于距离度量,该距离度量允许比较机器人与目标之间的差异。在本文中,我们提出了一种新颖的措施来生成具有ToM形态的机器人的各种行为。它包括一些距离度量,例如欧几里得距离,余弦距离,反正切距离和编辑距离。它通过神经网络为ToM生成机器人的各种行为。所提出的方法已经在真实的电子冰球机器人平台上进行了实验。

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