Automatic selection of coordinate systems for learning relative and absolute spatial concepts

Rikunari Sagara; Ryo Taguchi; Akira Taniguchi; Tadahiro Taniguchi

doi:10.3389/frobt.2022.904751

Automatic selection of coordinate systems for learning relative and absolute spatial concepts

Front Robot AI. 2022 Aug 12:9:904751. doi: 10.3389/frobt.2022.904751. eCollection 2022.

Authors

Rikunari Sagara¹, Ryo Taguchi¹, Akira Taniguchi², Tadahiro Taniguchi²

Affiliations

¹ Taguchi Laboratory, Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan.
² Emergent Systems Laboratory, College of Information Science and Engineering, Ritsumeikan University, Kyoto, Japan.

Abstract

Robots employed in homes and offices need to adaptively learn spatial concepts using user utterances. To learn and represent spatial concepts, the robot must estimate the coordinate system used by humans. For example, to represent spatial concept "left," which is one of the relative spatial concepts (defined as a spatial concept depending on the object's location), humans use a coordinate system based on the direction of a reference object. As another example, to represent spatial concept "living room," which is one of the absolute spatial concepts (defined as a spatial concept that does not depend on the object's location), humans use a coordinate system where a point on a map constitutes the origin. Because humans use these concepts in daily life, it is important for the robot to understand the spatial concepts in different coordinate systems. However, it is difficult for robots to learn these spatial concepts because humans do not clarify the coordinate system. Therefore, we propose a method (RASCAM) that enables a robot to simultaneously estimate the coordinate system and spatial concept. The proposed method is based on ReSCAM+O, which is a learning method for relative spatial concepts based on a probabilistic model. The proposed method introduces a latent variable that represents a coordinate system for simultaneous learning. This method can simultaneously estimate three types of unspecified information: coordinate systems, reference objects, and the relationship between concepts and words. No other method can estimate all these three types. Experiments using three different coordinate systems demonstrate that the proposed method can learn both relative and absolute spatial concepts while accurately selecting the coordinate system. The proposed approach can be beneficial for service robots to flexibly understand a new environment through the interactions with humans.

Keywords: bayesian nonparametrics; coordinate system selection; lexical acquisition; relative concept acquisition; spatial concept acquisition.