The Semantic Hierarchy in Robot Learning
The Semantic Hierarchy in Robot Learning
复制标题
机器人学习中的语义层次
DOI：
10.1007/978-1-4615-3184-5_6
复制
发表时间：
1992
期刊：
影响因子：
--
通讯作者：
David Pierce
中科院分区：
文献类型：
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作者：
B. Kuipers;R. Froom;W. Lee;David Pierce
我们一直在探索一种基于机器人感官、动作和空间环境知识类型层次结构的机器人学习方法。这种方法源于人类认知图的计算模型，该模型利用了大规模空间的程序、拓扑和度量知识之间的区别。最近，Kuipers 和 Byun 将这种语义层次方法扩展到与连续环境的连续感觉运动交互，证明了识别独特位置在机器人空间学习中的基本作用。我们当前的研究将语义层次结构框架扩展到三个方向： * 我们正在测试语义层次结构方法将从模拟机器人自然扩展到物理机器人的假设。我们预计它将显着简化机器人与世界的感觉运动交互。 *我们正在演示语义层次结构以及学习的拓扑和度量认知图如何支持运动控制低点的学习，逐步从低速、摩擦主导的运动转向高速、动量主导的运动。 *我们正在开发方法，让白板机器人能够探索和学习最初未解释的感觉运动系统的特性，直到它能够定义和执行控制法则，识别独特的地点和路径，从而达到空间语义层次结构的第一级。如果这些目标能够实现，我们将制定一个全面的计算模型，用于表示、学习和使用有关空间和行动的大量知识。除了这些知识的内在价值之外，语义层次结构方法对于其他领域的建模也应该很有用。
We have been exploring an approach to robot learning based on a hierarchy of types of knowledge of the robot''s senses, actions, and spatial environment. This approach grew out of a computational model of the human cognitive map that exploited the distinction between procedural, topological, and metrical knowledge of large-scale space. More recently, Kuipers and Byun extended this semantic hierarchy approach to continuous sensorimotor interaction with a continuous environment, demonstrating the fundamental role of identification of distinctive places in robot spatial learning. Our current research extends the semantic hierarchy framework in three directions: * We are testing the hypothesis that the semantic hierarchy approach will scale up naturally from simulated to physical robots. We expect that it will significantly simplify the robot''s sensorimotor interaction with the world. * We are demonstrating how the semantic hierarchy, and the learned topological and metrical cognitive map, supports learning of motion control lows, leading incrementally from low-speed, friction-dominated motion to high-speed, momentum dominated motion. * We are developing methods whereby a tabula rasa robot can explore and learn the properties of an initially uninterpreted sensorimotor system, to the point where it can define ad execute control laws, identify distinctive places and paths, and hence reach the first level of the spatial semantic hierarchy. If these goals can be achieved, we will have formulated a comprehensive computational model of the representation, learning and use of a substantial body of knowledge about space and action. In addition to the intrinsic value of this knowledge, the semantic hierarchy approach should be useful in modeling other domains.