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首页> 外文会议>The Fourth joint IEEE international conferences on development and learning and epigenetic robotics >Autonomous object modeling based on affordances for spatial organization of behavior
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Autonomous object modeling based on affordances for spatial organization of behavior

机译：基于能力的行为空间组织的自治对象建模

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We present an architecture for self-motivated agents to organize their behaviors in space according to possibilities of interactions afforded by initially unknown objects. The long-term goal is to design agents that construct their own knowledge of objects through experience, rather than exploiting pre-coded knowledge. Self-motivation is defined here as a tendency to experiment and to respond to behavioral opportunities afforded by the environment. Some interactions have predefined valences that specify inborn behavioral preferences. Over time, the agent learns the relation between its perception of objects and the interactions that they afford, in the form of data structures, called signatures of interaction, which encode the minimal spatial configurations that afford an interaction. The agent keeps track of enacted interactions in a topological spatial memory, to recognize and localize subsequent possibilities of interaction (through their signatures) afforded by surrounding objects. Experiments with a simulated agent and a robot show that they learn to navigate in their environment, taking into account multiple surrounding objects, reaching or avoiding objects according to the valence of the interactions that they afford.
机译：我们提出了一种自我激励的代理架构，可以根据最初未知对象提供的交互作用来组织他们在太空中的行为。长期目标是设计代理，以通过经验构建自己的对象知识，而不是利用预先编码的知识。自我激励在这里被定义为一种尝试并响应环境所提供的行为机会的趋势。一些交互具有预定义的价，这些价指定了先天的行为偏好。随着时间的流逝，代理会以数据结构的形式（称为交互签名）学习其对对象的感知与它们提供的交互之间的关系，该数据结构对提供交互的最小空间配置进行编码。该代理在拓扑空间内存中跟踪已制定的交互作用，以识别并定位周围对象提供的后续交互作用可能性（通过其签名）。用模拟的代理程序和机器人进行的实验表明，他们学会了在环境中导航，并考虑了周围的多个物体，并根据它们提供的交互作用的程度来达到或避开物体。

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