Various strategies have been explored for robots to prevent navigation blocks. However, such blocks may still happen and robots then need to resolve them. Blocks may happen either on the robot’s path or target location and may be caused either by human or object obstacles. In this paper we explore the design of robot behaviors to resolve such navigation blocks by asking humans for help. These behaviors combine different communication modalities in steps with increasing urgency. We focus on robots with limited sensing capabilities and present findings from an in-person experiment evaluating these behaviors. Our findings illustrate that humans can be more easily engaged to solve blocks caused by themselves rather than by third party objects. They also highlight the complexity of having robots with limited sensing capabilities successfully enact sequential interactions with their bystanders.

As robots increasingly share spaces with people, it becomes important for them to behave according to our social norms. In this paper, we explore the problem of finding socially acceptable locations for a robot to wait for a shared elevator by learning from expert annotations. Access to relevant, unlabeled data is however scarce in this setting and annotations expensive to gather, as they require explicit knowledge about the social norms, the robot, and the service it carries out. We tackle this low-data regime as follows. First, we use Procedural Content Generation to generate plausible waiting scenes to be annotated. Second, we leverage available sociological studies and operationalize relevant social norms as feature maps. We train a variety of models with only 125 procedurally-generated expert-annotated scenes, testing the impact of the proposed feature maps. In our ablation study, the feature maps help the models’ performance and their generalization capabilities to non-synthetic, real scenes. We inspect the decisions taken by the best models, probing their strengths and weaknesses, and identifying general issues and discussing potential solutions.

This paper examines the advantages and disadvantages of combining Human-Like and Machine-Like behaviors for a robot taking a shared elevator with a bystander as part of an office delivery service scenario. We present findings of an in-person wizard-of-oz experiment that builds on and implements behavior policies developed in a previous study. In this experiment, we found that the combination of Machine-Like and Human-Like behaviors was perceived as better than Human-Like behaviors alone. We discuss possible reasons and point to key capabilities that a socially competent robot should have to achieve better Human-Like behaviors in order to seamlessly negotiate a social encounter with bystanders in a shared elevator or similar scenario. We found that establishing and maintaining a shared transactional space is one of these key requirements.

Runners want to actively explore unknown environments without the fear of getting lost. We conducted a survey to better understand runners’ needs and practices in this context. The survey results emphasized the interest in flexible exploration. To address this interest, we designed FlexNav, a system that supports exploratory running through flexible tours that link the best runnable zones in a neighbourhood. FlexNav provides adaptive navigation support enabling runners to follow such tours without continually getting disruptive directions. We tested it with runners to assess its usability. The results confirm the usefulness of the system and the users' preference for flexible tours over fully specified tours with turn-by-turn guidance. Our study highlights the subjective nature of runnable zones and the subtle balance between guidance and exploration.

The design and development of robots involve the essential step of selecting and testing robotic interfaces. This interface selection requires careful consideration as the robot’s physical embodiment influences and adds to the traditional interfaces’ complexities. Our paper presents a decision support design framework for the a priori selection of robotic interface that was inductively formulated from our case study of designing a robot to collaborate with employees with cognitive disabilities. Our main contribution is to provide a novel framework that outlines the interface requirements according to user, robot, tasks and environment and facilitates a structured comparison of interfaces against those requirements. The framework is assessed for its potential applicability and usefulness through a qualitative study with HRI experts. The framework is appreciated as a systematic tool that enables documentation and discussion, and identified issues inform the framework’s iteration. The themes of ownership of this process in interdisciplinary teams and its role in iteratively designing interfaces are discussed.

In this paper, we present our ongoing research on socially acceptable robot navigation for an indoor elevator sharing scenario. Informed by naturalistic observations of human elevator use, we discuss the social nuances involved in a seemingly simple activity like taking an elevator and the challenges and limitations of modeling robot behaviors based on a full human-like approach. We propose the principle of machine-like for the design of robot behavior policies that effectively accomplish tasks without being disruptive to the routines of people sharing the elevator with the robots. We explored this approach in a bodystorming session and conducted a preliminary evaluation of the resulting considerations through an online user study. Partic-ipants differentiated robots from humans for issues of proxemics and priority, and machine-like behaviors were preferred over human-like behaviors. We present our findings and discuss the advantages and limitations identified for both approaches for designing socially acceptable navigation behaviors.

Navigation systems for runners commonly provide turn-by-turn directions via voice and/or map-based visualizations. While voice directions require permanent attention, map-based guidance requires regular consultation. Both disrupt the running activity. To address this, we designed RunAhead, a navigation system using head scanning to query for navigation feedback, and we explored its suitability for runners in an outdoor experiment. In our design, we provide the runner with simple and intuitive navigation feedback on the path s/he is looking at through three different feedback modes: haptic, music and audio cues. In our experiment, we compare the resulting three versions of RunAhead with a baseline voice-based navigation system. We find that demand and error are equivalent across all four conditions. However, the head scanning based haptic and music conditions are preferred over the baseline and these preferences are impacted by runners' habits. With this study we contribute insights for designing navigation support for runners.