Learning Object Orientation Constraints and Guiding Constraints for Narrow Passages from One Demonstration
Learning Object Orientation Constraints and Guiding Constraints for Narrow Passages from One Demonstration
复制标题
从一次演示中学习对象定向约束和狭窄通道的引导约束
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
D. Berenson
中科院分区：
文献类型：
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作者：
Changshuo Li;D. Berenson
狭窄通道和方向约束在操作任务中非常常见，在这种情况下，基于采样的规划方法可能会非常耗时。我们提出了一种方法，该方法可以通过分析演示轨迹周围的几何形状，从单个人类演示中学习物体方向约束和引导约束，并将其表示为任务空间区域。我们方法的关键思想是通过在任务空间中采样来探索演示轨迹周围的区域，并通过对可行样本进行分割和分析来学习约束。我们的方法在一个包含四个子任务的换轮胎场景和一个取杯子任务上进行了测试。我们的结果表明，我们的方法能够在不到3分钟的时间内为所有这些任务生成计划，并且所有任务的成功试验率为50/50，而基线方法在30分钟内对其中一个任务仅在50次中有1次成功。结果还表明，我们的方法能够在有额外障碍物的情况下执行类似任务，转移到具有不同起始和/或目标姿态的类似任务，并可用于PR2机器人的实际任务。
Narrow passages and orientation constraints are very common in manipulation tasks and sampling-based planning methods can be quite time-consuming in such scenarios. We propose a method that can learn object orientation constraints and guiding constraints, represented as Task Space Regions, from a single human demonstrations by analyzing the geometry around the demonstrated trajectory. The key idea of our method is to explore the area around the demonstration trajectory through sampling in task space, and to learn constraints by segmenting and analyzing the feasible samples. Our method is tested on a tire-changing scenario which includes four sub-tasks and on a cup-retrieving task. Our results show that our method can produce plans for all these tasks in less than 3 min with 50 / 50 successful trials for all tasks, while baseline methods only succeed 1 out of 50 times in 30 min for one of the tasks. The results also show that our method can perform similar tasks with additional obstacles, transfer to similar tasks with different start and/or goal poses, and be used for real-world tasks with a PR2 robot.