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Abstract Bringing human–robot interaction (HRI) into conversation with scholarship from human geography, this paper considers how socially interactive robots become important agents in the production of social space and explores the utility of core geographic concepts ofscaleandplaceto critically examine evolving robotic spatialities. The paper grounds this discussion through reflections on a collaborative, interdisciplinary research project studying the development and deployment of interactive museum tour-guiding robots on a North American university campus. The project is a collaboration among geographers, roboticists, a digital artist, and the directors/curators of two museums, and involves experimentation in the development of a tour-guiding robot with a “socially aware navigation system” alongside ongoing critical reflection into the socio-spatial context of human–robot interactions and their future possibilities. The paper reflects on the tensions between logics of control and contingency in robotic spatiality and argues that concepts of scale and place can help reflect on this tension in a productive way while calling attention to a broader range of stakeholders who should be included in robotic design and deployment. 

In an efficient and flexible human-robot collaborative work environment, a robot team member must be able to recognize both explicit requests and implied actions from human users. Identifying “what to do” in such cases requires an agent to have the ability to construct associations between objects, their actions, and the effect of actions on the environment. In this regard, semantic memory is being introduced to understand the explicit cues and their relationships with available objects and required skills to make “tea” and “sandwich”. We have extended our previous hierarchical robot control architecture to add the capability to execute the most appropriate task based on both feedback from the user and the environmental context. To validate this system, two types of skills were implemented in the hierarchical task tree: 1) Tea making skills and 2) Sandwich making skills. During the conversation between the robot and the human, the robot was able to determine the hidden context using ontology and began to act accordingly. For instance, if the person says “I am thirsty” or “It is cold outside” the robot will start to perform the tea-making skill. In contrast, if the person says, “I am hungry” or “I need something to eat”, the robot will make the sandwich. A humanoid robot Baxter was used for this experiment. We tested three scenarios with objects at different positions on the table for each skill. We observed that in all cases, the robot used only objects that were relevant to the skill. 

Human-robot interaction (HRI) studies have found people overtrust robots in domestic settings, even when the robot exhibits faulty behavior. Cognitive dissonance and selective attention explain these results. To test these theories, a novel HRI study was performed in a university library where participants were recruited to follow a package delivery robot. Participants then faced a dilemma to deliver a package in a private common room that might be off-limits. Then, they faced another dilemma when the robot stopped in front of an Emergency Exit door, and they had to trust the robot whether to open it or not Results showed individuals did not overtrust the robot and open the Emergency Exit door. Interestingly, most individuals demurred from entering the private common room when packages were not labeled, whereas groups of friends were more likely to enter the room. Then, selective attention was demonstrated by stopping participants in front of a similar Emergency Exit door and assessing whether they noticed it In one condition, only half of participants noticed it, and when the robot became more engaging no one noticed it. Additionally, a malfunctioning robot is exhibited, showing what kind of negative outcome was required to reduce trust. 

Mobile robots must navigate efficiently, reliably, and appropriately around people when acting in shared social environments. For robots to be accepted in such environments, we explore robot navigation for the social contexts of each setting. Navigating through dynamic environments solely considering a collision-free path has long been solved. In human-robot environments, the challenge is no longer about efficiently navigating from one point to another. Autonomously detecting the context and adapting to an appropriate social navigation strategy is vital for social robots’ long-term applicability in dense human environments. As complex social environments, museums are suitable for studying such behavior as they have many different navigation contexts in a small space.Our prior Socially-Aware Navigation model considered con-text classification, object detection, and pre-defined rules to define navigation behavior in more specific contexts, such as a hallway or queue. This work uses environmental context, object information, and more realistic interaction rules for complex social spaces. In the first part of the project, we convert real-world interactions into algorithmic rules for use in a robot’s navigation system. Moreover, we use context recognition, object detection, and scene data for context-appropriate rule selection. We introduce our methodology of studying social behaviors in complex contexts, different analyses of our text corpus for museums, and the presentation of extracted social norms. Finally, we demonstrate applying some of the rules in scenarios in the simulation environment.