Motion Synthesis, Learning and Abstraction through Parameterized Smooth Map from Sensors to Behaviors
Motion Synthesis, Learning and Abstraction through Parameterized Smooth Map from Sensors to Behaviors
复制标题
通过从传感器到行为的参数化平滑映射进行运动合成、学习和抽象
DOI：
10.1007/978-1-4471-1580-9_7
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
Nagamasa Mizushima
中科院分区：
文献类型：
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作者：
Y. Nakamura;T. Yamazaki;Nagamasa Mizushima
本文将讨论反应行为的整合理论。采用线性新兴模型，其中机器人的运动表示为反应行为的加权线性和。权重被定义为传感器信号和参数的可微非线性函数。这些函数可以将逻辑 if-then 规则表示为其极端情况。引入传感器空间模型来将传感器和行为联系起来并确定参数。我们建立了一种基于传感器空间模型的学习方法，通过迭代试验系统地调整参数，使传感器信号收敛到给定的教师信号。还提出了传感器空间模型中的非线性动力学，以允许未来全局搜索的波动。该学习方法应用于三指机器人手的反应式抓取。我们集成了 48 种传感器信号和 29 种原始行为。实验表明，新兴模型允许我们使用语义来最初对权重的非线性函数进行编程。学习实验成功地说明了所提出的学习方法的有用性。
The integration theory of reactive behaviors is to be discussed in this paper. A linear emerging model is adopted where the motion of a robot is represented as the weighted linear sum of reactive behaviors. The weights are defined as differentiable nonlinear functions of sensor signals and parameters. The functions can represent logical if-then rules as their extreme cases. The sensor space model is introduced to relate the sensors and the behaviors and to determine the parameters. We establish a learning method based on the sensor space model, where the parameters are systematically tuned through iteration of trials such that the sensor signals converge to the given teacher signals. A nonlinear dynamics in the sensor space model is also proposed to allow fluctuation for the future global search. The learning method is applied to the reactive grasp of a three-fingered robot hand. We integrate 48 kinds of sensor signals and 29 primitive behaviors. The experiments indicate that the emerging model allows us to use the semantics to initially program the nonlinear functions for the weights. The learning experiments successfully illustrate the usefulness of the proposed learning method.