使用监督学习对服务机器人的操作活动进行监控

S. Ruehl, Zhixing Xue, R. Dillmann
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引用次数: 1

摘要

为了成为一个好的助手,抓取和操纵是服务机器人最重要的能力。它应该能够使其操作操作适应新的任务和环境。在执行过程中,重要的是评估动作的成功程度,以便机器人可以计划和执行进一步的动作,以纠正和恢复失败的动作。操作动作的成功执行取决于整个执行过程中的各种因素,如机械手的位置和机器人施加的力。操作操作监视的目标是从执行期间收集的大量数据中估计成功状态。解决该问题的主要挑战是从高维数据集合中识别成功或失败状态。我们提出了一种使用一组支持向量机(SVM)对正在进行的活动进行分类的方法。经过人工标记成功或失败结果的监督训练过程后,我们的系统可以正确地估计操作活动的结果状态。我们在我们的双手操作演示器上进行了实验并评估了结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring of manipulation activities for a service robot using supervised learning
To be a good helper, grasping and manipulation are the most important abilities of a service robot. It should be able to adapt its manipulation actions to new tasks and environments. During the execution, it is important to rate the success of actions, so that the robot can plan and execute further actions to correct and recover from the failed actions. The successful execution of manipulation actions depends on various factors during the whole execution, such as the position of the robotic hand and forces exerted by the robot. The goal of the manipulation action monitoring is to estimate the success state from the huge amount of data collected during the execution. The main challenge to solve this problem is to identify the success or failure state from the the high dimensional data collection. We propose a method to classify ongoing activities using a set of support vector machines (SVM). After a supervised training process with manually labeled successful or failure results, our system can correctly estimate the resulting state of a manipulation activity. We present experiments on our bimanual manipulation demonstrator and evaluate the results.
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