Inferring Place-Object Relationships by Integrating Probabilistic Logic and Multimodal Spatial Concepts
Inferring Place-Object Relationships by Integrating Probabilistic Logic and Multimodal Spatial Concepts
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DOI：
10.1109/sii55687.2023.10039318
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Shoichi Hasegawa;Akira Taniguchi;Y. Hagiwara;Lotfi El Hafi;T. Taniguchi
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作者：
Shoichi Hasegawa;Akira Taniguchi;Y. Hagiwara;Lotfi El Hafi;T. Taniguchi
我们提出了一种新颖的方法，该方法将概率逻辑和多模式空间概念整合在一起，以使机器人能够在新的环境中获得一些学习时间的新环境中的位置和对象之间的关系。使用具有概率值的谓词逻辑（即概率逻辑）来表示对位置对象关系的常识知识，我们使用概率逻辑与交叉模式推断结合了逻辑推断，该推断可以计算出一个模态的其他模态的条件概率。这允许机器人推断物体的位置即使不知道对象在家庭环境中的可能位置，也可以找到该物体的位置。我们进行了实验，其中一个机器人在模拟的家庭环境中使用四种方法在模拟家庭环境中搜索日常对象，包括：1）仅多模式空间概念，2）仅常识性知识，3）常识性知识和多模式概念以及多模式概念以及多模态概念4）概率逻辑和多模式的空间概念（提出）。我们通过比较机器人找到所有对象所需的地点访问次数来确认所提出的方法的有效性。我们还观察到，当机器人执行在新的家庭环境中找到具有不确定位置的对象时，我们提出的方法在三种基线方法上将现场学习成本降低了1.6。
We propose a novel method that integrates probabilistic logic and multimodal spatial concepts to enable a robot to acquire the relationships between places and objects in a new environment with a few learning times. Using predicate logic with probability values (i.e., probabilistic logic) to represent commonsense knowledge of place-object relationships, we combine logical inference using probabilistic logic with the cross-modal inference that can calculate the conditional probabilities of other modalities given one modality. This allows the robot to infer the place of the object to find even when it does not know the likely place of the object in the home environment. We conducted experiments in which a robot searched for daily objects, including objects with undefined places, in a simulated home environment using four approaches: 1) multimodal spatial concepts only, 2) commonsense knowledge only, 3) commonsense knowledge and multimodal spatial concepts, and 4) probabilistic logic and multimodal spatial concepts (proposed). We confirmed the effectiveness of the proposed method by comparing the number of place visits it took for the robot to find all the objects. We also observed that our proposed approach reduces the on-site learning cost by a factor of 1.6 over the three baseline methods when the robot performs the task of finding objects with undefined places in a new home environment.