Probabilistic Effect Prediction through Semantic Augmentation and Physical Simulation

A. Bauer, Peter Schmaus, F. Stulp, Daniel Leidner
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引用次数: 11

Abstract

Nowadays, robots are mechanically able to perform highly demanding tasks, where AI-based planning methods are used to schedule a sequence of actions that result in the desired effect. However, it is not always possible to know the exact outcome of an action in advance, as failure situations may occur at any time. To enhance failure tolerance, we propose to predict the effects of robot actions by augmenting collected experience with semantic knowledge and leveraging realistic physics simulations. That is, we consider semantic similarity of actions in order to predict outcome probabilities for previously unknown tasks. Furthermore, physical simulation is used to gather simulated experience that makes the approach robust even in extreme cases. We show how this concept is used to predict action success probabilities and how this information can be exploited throughout future planning trials. The concept is evaluated in a series of real world experiments conducted with the humanoid robot Rollin’ Justin.
基于语义增强和物理模拟的概率效应预测
如今,机器人在机械上能够执行高要求的任务,其中基于人工智能的规划方法被用来安排一系列动作,从而产生预期的效果。然而,提前知道操作的确切结果并不总是可能的,因为故障情况随时可能发生。为了提高故障容忍度,我们建议通过使用语义知识和利用现实物理模拟来增加收集到的经验来预测机器人动作的影响。也就是说,我们考虑动作的语义相似性,以预测先前未知任务的结果概率。此外,物理模拟用于收集模拟经验,使该方法即使在极端情况下也具有鲁棒性。我们展示了如何使用这个概念来预测行动成功的概率,以及如何在未来的计划试验中利用这些信息。这一概念在一系列真实世界的实验中得到了评估,这些实验是由人形机器人Rollin ' Justin进行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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