Do You Mind if I Pass Through? Studying the Appropriate Robot Behavior when Traversing two Conversing People in a Hallway Setting

Petrak, Bjorn; Sopper, Gundula; Weitz, Katharina; André, Elisabeth

doi:10.1109/ro-man50785.2021.9515430

Cited by 5 publications

(6 citation statements)

References 26 publications

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“…The use of a video set does provide some notable strengths for exploring this research design. This format further supports the perception of social acceptance from mobile robots compared to other video-based studies on the topic [21][22][23]28]. This includes the capacity to collect a broad sample from other countries and to provide further explanation of robot behaviour for each scenario presented in the video set.…”

Section: Limitations and Future Workmentioning

confidence: 52%

“…Mobile robots that communicate their intended movements to the person were found to increase social acceptance [19,20]. In a scenario where the mobile robot must pass between two people, people expected robots to exhibit social cues, so that people can better anticipate its intended behaviour and for the robot to be as non-disruptive as possible [21]. The ease with which robot behaviour could be understood increased perceived safety in a path crossing scenario tested using first-person videos [22], and through communication tools to indicate when the robot will yield to a pedestrian in the hallway [23].…”

Section: Socially Acceptable Behavioursmentioning

confidence: 99%

“…An online questionnaire design was used to conduct this experiment. Video studies represent a common way to measure social acceptance of mobile robots in home and office environments [21][22][23]28]. An online design can capture rapid insight into the navigation method from a large number of people around the world, increasing the external relevance of the results.…”

Section: Designmentioning

confidence: 99%

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Should I Just Stick to the Wall? Evaluating the Social Acceptance and Preferred Driving Side of Wall Following

Robinson,

Yeung,

Cosgun

2024

Int J of Soc Robotics

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The need for safe, predictable, and reliable robot navigation is fundamental for mobile robots to move around in home and office environments. Shortest-path navigation is a popular robot navigation method that uses the most efficient path to get to the desired goal. This behaviour is not always easy to interpret, understand, and avoid in a congested hallway. Instead, more predictable navigation methods, such as a robot following a wall, can help increase social acceptance and help avoid the robot crossing the pedestrian path. If a robot follows along a wall, a key variable to consider is the preferred driving side of the robot (left or right) in areas such as in narrow passages, and its perceived impact on social acceptance. This international user study (n = 143) involved an online video-based test to compare robot evaluation and social acceptance for two types of mobile navigation (Wall-Following and Shortest Path), including the preferred driving side for Wall-Following. A Fetch robot navigated from start to goal position in a series of indoor scenarios with a pedestrian. Select scenarios included a hallway, doorway, and intersection. Independent Sample T-Tests results found that Wall-Following was rated significantly higher than Shortest Path for being perceived as more comfortable and predictable, regardless of robot driving side. The preference for the driver side of the robot did not match the country of residence, nor did it have a significant impact on robot ratings. There were significant interaction effects for comfort, safety and predictable scores across two timepoints. Given the popularity of Shortest Path navigation, the findings indicate that this approach might not be the most appropriate for human settings. Additional investigation into Wall-Following behaviours is recommended for social acceptance, even if the method compromises the efficiency of the robot to acheive its objective.

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Section: Limitations and Future Workmentioning

confidence: 52%

Section: Socially Acceptable Behavioursmentioning

confidence: 99%

Section: Designmentioning

confidence: 99%

See 1 more Smart Citation

Should I Just Stick to the Wall? Evaluating the Social Acceptance and Preferred Driving Side of Wall Following

Robinson,

Yeung,

Cosgun

2024

Int J of Soc Robotics

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“…An example phenomenon that was not targeted by user studies to the extent that allows establishing specific principles is facial expressions. Petrak et al [71] discussed a side note of their study that enhanced robot facial expressions and gestures could make the behavior easier to anticipate for the experiment participants. Kruse et al [15] pointed out additional navigation conventions, such as giving priority to elderly people at doorways, asking for permission to pass, and excusing oneself when one has to traverse a personal zone to reach a goal.…”

Section: Discussionmentioning

confidence: 99%

“…A general guideline for robots navigating through populated environments is to avoid cutting through social groups [70], but if it is not possible, e.g., in a narrow corridor, they should politely pass through the O-space [12,71].…”

Section: Avoiding Crossing Through Human Groups (Req 22)mentioning

confidence: 99%

Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation

Karwowski,

Szynkiewicz,

Niewiadomska-Szynkiewicz

2024

Sensors

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Navigation lies at the core of social robotics, enabling robots to navigate and interact seamlessly in human environments. The primary focus of human-aware robot navigation is minimizing discomfort among surrounding humans. Our review explores user studies, examining factors that cause human discomfort, to perform the grounding of social robot navigation requirements and to form a taxonomy of elementary necessities that should be implemented by comprehensive algorithms. This survey also discusses human-aware navigation from an algorithmic perspective, reviewing the perception and motion planning methods integral to social navigation. Additionally, the review investigates different types of studies and tools facilitating the evaluation of social robot navigation approaches, namely datasets, simulators, and benchmarks. Our survey also identifies the main challenges of human-aware navigation, highlighting the essential future work perspectives. This work stands out from other review papers, as it not only investigates the variety of methods for implementing human awareness in robot control systems but also classifies the approaches according to the grounded requirements regarded in their objectives.

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An Interpretable Monitoring Framework for Virtual Physics-Based Non-Interfering Robot Social Planning

Peddi

Bezzo

2022

IEEE Robot. Autom. Lett.

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Autonomous mobile robots (AMR) operating in the real world often need to make critical decisions that directly impact their own safety and the safety of their surroundings. Learning-based approaches for decision making have gained popularity in recent years, since decisions can be made very quickly and with reasonable levels of accuracy for many applications. These approaches, however, typically return only one decision, and if the learner is poorly trained or observations are noisy, the decision may be incorrect. This problem is further exacerbated when the robot is making decisions about its own failures, such as faulty actuators or sensors and external disturbances, when a wrong decision can immediately cause damage to the robot. In this paper, we consider this very case study: a robot dealing with such failures must quickly assess uncertainties and make safe decisions. We propose an uncertainty aware learning-based failure detection and recovery approach, in which we leverage Decision Tree theory along with Model Predictive Control to detect and explain which failure is compromising the system, assess uncertainties associated with the failure, and lastly, find and validate corrective controls to recover the system. Our approach is validated with simulations and real experiments on a faulty unmanned ground vehicle (UGV) navigation case study, demonstrating recovery to safety under uncertainties.

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Do You Mind if I Pass Through? Studying the Appropriate Robot Behavior when Traversing two Conversing People in a Hallway Setting

Cited by 5 publications

References 26 publications

Should I Just Stick to the Wall? Evaluating the Social Acceptance and Preferred Driving Side of Wall Following

Should I Just Stick to the Wall? Evaluating the Social Acceptance and Preferred Driving Side of Wall Following

Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation

An Interpretable Monitoring Framework for Virtual Physics-Based Non-Interfering Robot Social Planning

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