Learning the Geometric Meaning of Symbolic Abstractions for Manipulation Planning
Learning the Geometric Meaning of Symbolic Abstractions for Manipulation Planning
复制标题
学习操纵规划的符号抽象的几何意义
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
R. Dearden
中科院分区：
文献类型：
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作者：
Christopher Burbridge;R. Dearden
我们提出了一种学习几何状态和逻辑谓词之间的映射的方法。这种映射是任何需要任务级推理和路径规划的机器人系统的必要组成部分。考虑一个机器人的任务是把一些杯子放在托盘上。为了实现这个目标，机器人需要找到所有物体的位置，如果有必要，可能需要将一个杯子堆叠在另一个杯子中，以便将它们全部放在托盘上。这需要在规划者使用的符号状态（如“堆叠（cup1，cup2）”）和表示对象位置和姿态的几何状态之间来回转换。我们在本文中学习的映射实现了这种翻译。我们从标记的示例中学习它，并且重要的是，学习一种可以在正向（从几何到符号）和反向方向上使用的表示。这使我们能够构建机器人观察到的场景的符号表示，并将所需的符号状态从计划转换为机器人实际上可以通过操作实现的几何状态。我们还展示了如何使用该方法来生成显着不同的几何解决方案，以支持回溯。我们在模拟和机器人手臂上的工作进行评估。
We present an approach for learning a mapping between geometric states and logical predicates. This mapping is a necessary part of any robotic system that requires task-level reasoning and path planning. Consider a robot tasked with putting a number of cups on a tray. To achieve the goal the robot needs to find positions for all the objects, and if necessary may need to stack one cup inside another to get them all on the tray. This requires translating back and forth between symbolic states that the planner uses such as “stacked(cup1,cup2)” and geometric states representing the positions and poses of the objects. The mapping we learn in this paper achieves this translation. We learn it from labelled examples, and significantly, learn a representation that can be used in both the forward (from geometric to symbolic) and reverse directions. This enables us to build symbolic representations of scenes the robot observes, and also to translate a desired symbolic state from a plan into a geometric state that the robot can actually achieve through manipulation. We also show how the approach can be used to generate significantly different geometric solutions to support backtracking. We evaluate the work both in simulation and on a robot arm.