# Coordinating Entrainment Phenomena: Robot Conversation Strategy for Object Recognition
###### Abstract  
This study proposes a robot conversation strategy involving speech and gestures to improve a robot's indicated object recognition, i.e., the recognition of an object indicated by a human. Research conducted to improve the performance of indicated object recognition is divided into two main approaches: development and interactive. The development approach addresses the development of new devices or algorithms. Through human-robot interaction, the interactive approach improves the performance by decreasing the variability and the ambiguity of the references. Inspired by the findings of entrainment and entrainment inhibition, this study proposes a robot conversation strategy that utilizes the interactive approach. While entrainment is a phenomenon in which people unconsciously tend to mimic words and/or gestures of their interlocutor, entrainment inhibition is the opposite phenomenon in which people decrease the amount of information contained in their words and gestures when their interlocutor provides excess information. Based on these phenomena, we designed a robot conversation strategy that elicits clear references. We experimentally compared this strategy with the other interactive strategy in which a robot explicitly requests clarifications when a human refers to an object. We obtained the following findings: (1) The proposed strategy clarifies human references and improves indicated object recognition performance, and (2) the proposed strategy forms better impressions than the other interactive strategy that explicitly requests clarifications when people refer to objects. human-robot interaction; indicated object recognition; entrainment; alignment 2021 4 March 2021 7 March 2021 72021  
Although such developments enhance the sensing capabilities of robots, recognizing the objects indicated by users remains difficult because humans' reference behaviors are often ambiguous during conversations. People enjoy enormous variability in their lexical choices to refer to a thing [5], which degrades the recognition performance because they might not always use the lexical expression contained in a database that stores an object's characteristics, and they do not always use enough words to identify an object [6]. Even if a robot can perfectly recognize speech and/or pointing gestures, they might not be able to identify an indicated object by humans from other objects. How to solve such ambiguity of referencing is an important issue for the indicated object recognition. The interactive approach aims to improve the performance by decreasing such diversity and the ambiguity of referencing through human-robot interaction. For example, if a robot explains how to refer to an object, people can know a way of referencing and the ambiguity may reduce. The interactive approach tries to change human behaviors to be more favorable for recognition; as a result, the recognition performance could improve. The interactive approach is considered to be useful because it does not require any special equipment and can be used together with the development approach. In this study, we focus on the interactive approach and propose a new strategy of the interactive approach. Implicit and Explicit Dyad: Two Types of Interaction Strategies to Clear the Ambiguity of Referencing Behavior  
A simple strategy of the interactive approach is the explicit request. Based on the premise that humans refer to an object in an ambiguous way, some researchers have proposed an interactive strategy in which a robot explicitly asks users to provide additional information to identify an indicated object [1; 7; 8; 9]. For example, [PERSON] et al. proposed a system that integrates deep learning-based object detection and natural language processing to calculate the ambiguity of referencing and request additional information for the ambiguous references [1]. [PERSON] et al. proposed a robotic system that requested users to provide additional information about an object that was difficult to detect [8].