Extracting kinematic background knowledge from interactions using task-sensitive relational learning
Extracting kinematic background knowledge from interactions using task-sensitive relational learning
复制标题
使用任务敏感的关系学习从交互中提取运动学背景知识
DOI：
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复制
发表时间：
2014
期刊：
影响因子：
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通讯作者：
O. Brock
中科院分区：
文献类型：
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作者：
S. Höfer;Tobias Lang;O. Brock
为了成功地操纵新的物体，机器人必须首先获得有关物体运动结构的信息。我们提出了一种从与世界的探索性交互中学习相关运动学背景知识的方法。随着机器人积累经验，这种背景知识使得能够以更高的效率获取运动学世界模型。然而，学习这样的背景知识被证明是困难的，特别是在复杂的，功能丰富的领域。我们提出了一种新的，任务敏感的关系规则学习器，并证明它能够学习准确的运动学背景知识的领域，其他方法失败。由此产生的背景知识是更紧凑，更好地概括与现有的方法。
To successfully manipulate novel objects, robots must first acquire information about the objects' kinematic structure. We present a method for learning relational kinematic background knowledge from exploratory interactions with the world. As the robot gathers experience, this background knowledge enables the acquisition of kinematic world models with increasing efficiency. Learning such background knowledge, however, proves difficult, especially in complex, feature-rich domains. We present a novel, task-sensitive relational rule learner and demonstrate that it is able to learn accurate kinematic background knowledge in domains where other approaches fail. The resulting background knowledge is more compact and generalizes better than that obtained with existing approaches.
影响因子：
5
作者：
Sturm, Juergen;Stachniss, Cyrill;Burgard, Wolfram
通讯作者：
Burgard, Wolfram