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Incremental Acquisition of Verb Hypothesis Space towards Physical World Interaction

机译:物理世界互动中动词假设空间的增量获取

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As a new generation of cognitive robots start to enter our lives, it is important to enable robots to follow human commands and to learn new actions from human language instructions. To address this issue, this paper presents an approach that explicitly represents verb semantics through hypothesis spaces of fluents and automatically acquires these hypothesis spaces by interacting with humans. The learned hypothesis spaces can be used to automatically plan for lower-level primitive actions towards physical world interaction. Our empirical results have shown that the representation of a hypothesis space of fluents, combined with the learned hypothesis selection algorithm, outperforms a previous baseline. In addition, our approach applies incremental learning, which can contribute to life-long learning from humans in the future.
机译:随着新一代认知机器人开始进入我们的生活,使机器人能够遵循人类命令并从人类语言指令中学习新的动作非常重要。为了解决这个问题,本文提出了一种方法,该方法通过流利的假设空间来明确表示动词语义,并通过与人互动来自动获取这些假设空间。所学习的假设空间可用于自动规划针对物理世界交互的较低级别的原始动作。我们的经验结果表明,结合所学的假设选择算法,流利的假设空间的表示要优于先前的基线。此外,我们的方法还应用了增量学习,这将有助于未来人类的终身学习。

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