An official website of the United States government
Deployed social robots are increasingly relying on wakeword-based interaction, where interactions are human-initiated by a wakeword like “Hey Jibo”. While wakewords help to increase speech recognition accuracy and ensure privacy, there is concern that wakeword-driven interaction could encourage impolite behavior because wakeword-driven speech is typically phrased as commands. To address these concerns, companies have sought to use wake- word design to encourage interactant politeness, through wakewords like “⟨Name⟩, please”. But while this solution is intended to encourage people to use more “polite words”, researchers have found that these wakeword designs actually decrease interactant politeness in text-based communication, and that other wakeword designs could better encourage politeness by priming users to use Indirect Speech Acts. Yet there has been no previous research to directly compare these wakewords designs in in-person, voice-based human-robot interaction experiments, and previous in-person HRI studies could not effectively study carryover of wakeword-driven politeness and impoliteness into human-human interactions. In this work, we conceptually reproduced these previous studies (n=69) to assess how the wakewords “Hey ⟨Name⟩”, “Excuse me ⟨Name⟩”, and “⟨Name⟩, please” impact robot-directed and human-directed politeness. Our results demonstrate the ways that different types of linguistic priming interact in nuanced ways to induce different types of robot-directed and human-directed politeness.
more »
« less
“Excuse Me, Robot”: Impact of Polite Robot Wakewords on Human-Robot Politeness
While the ultimate goal of natural-language based Human-Robot Interaction (HRI) may be free-form, mixed-initiative dialogue,social robots deployed in the near future will likely primarily engage in wakeword-driven interaction, in which users’ commands are prefaced by a wakeword such as “Hey, Robot.” This style of interaction helps to allay user privacy concerns, as the robot’s full speech recognition module need not be employed until the target wakeword is used. Unfortunately, there are a number of concerns in the popular media surrounding this style of interaction, with consumers fearing that it is training users (in particular,children) to be rude towards technology, and by extension, rude towards other humans. In this paper, we present a study that demonstrates how an alternate style of wakeword, i.e., “Excuse me, Robot” may allay this concern, by priming users to phrase commands as Indirect Speech Acts
more »
« less
- PAR ID:
- 10185832
- Date Published:
- Journal Name:
- International Conference on Social Robotics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Machine learning models that sense human speech, body placement, and other key features are commonplace in human-robot interaction. However, the deployment of such models in themselves is not without risk. Research in the security of machine learning examines how such models can be exploited and the risks associated with these exploits. Unfortunately, the threat models of risks produced by machine learning security do not incorporate the rich sociotechnical underpinnings of the defenses they propose; as a result, efforts to improve the security of machine learning models may actually increase the difference in performance across different demographic groups, yielding systems that have risk mitigation that work better for one group than another. In this work, we outline why current approaches to machine learning security present DEI concerns for the human-robot interaction community and where there are open areas for collaboration.more » « less
-
End-user development (EUD) represents a key step towards making robotics accessible for experts and non-experts alike. Within academia, researchers investigate novel ways that EUD tools can capture, represent, visualize, analyze, and test developer intent. At the same time, industry researchers increasingly build and ship programming tools that enable customers to interact with their robots. However, despite this growing interest, the role of EUD within HRI is not well defined. EUD struggles to situate itself within a growing array of alternative approaches to application development, such as robot learning and teleoperation. EUD further struggles due to the wide range of individuals who can be considered end users, such as independent third-party application developers, consumers, hobbyists, or even employees of the robot manufacturer. Key questions remain such as how EUD is justified over alternate approaches to application development, which contexts EUD is most suited for, who the target users of an EUD system are, and where interaction between a human and a robot takes place, amongst many other questions. We seek to address these challenges and questions by organizing the frst End-User Development for Human-Robot Interaction (EUD4HRI) workshop at the 2024 International Conference of Human-Robot Interaction. The workshop will bring together researchers with a wide range of expertise across academia and industry, spanning perspectives from multiple subfields of robotics, with the primary goal being a consensus of perspectives about the role that EUD must play within human-robot interaction.more » « less
-
Advances in robotics have contributed to the prevalence of human-robot collaboration (HRC). Working and interacting with collaborative robots in close proximity can be psychologically stressful. Therefore, it is important to understand the impacts of human-robot interaction (HRI) on mental stress to promote psychological well-being at the workplace. To this end, this study investigated how the HRI presence, complexity, and modality affect psychological stress in humans and discussed possible HRI design criteria during HRC. An experimental setup was implemented in which human operators worked with a collaborative robot on a Lego assembly task, using different interaction paradigms involving pressing buttons, showing hand gestures, and giving verbal commands. The NASA-Task Load Index, as a subjective measure, and the physiological galvanic skin conductance response, as an objective measure, were used to assess the levels of mental stress. The results revealed that the introduction of interactions during HRC helped reduce mental stress and that complex interactions resulted in higher mental stress than simple interactions. Meanwhile, the use of certain interaction modalities, such as verbal commands or hand gestures, led to significantly higher mental stress than pressing buttons, while no significant difference on mental stress was found between showing hand gestures and giving verbal commands.more » « less
-
This work challenges the common assumption in physical human-robot interaction (pHRI) that the movement intention of a human user can be simply modeled with dynamic equations relating forces to movements, regardless of the user. Studies in physical human-human interaction (pHHI) suggest that interaction forces carry sophisticated information that reveals motor skills and roles in the partnership and even promotes adaptation and motor learning. In this view, simple force-displacement equations often used in pHRI studies may not be sufficient. To test this, this work measured and analyzed the interaction forces (F) between two humans as the leader guided the blindfolded follower on a randomly chosen path. The actual trajectory of the follower was transformed to the velocity commands (V) that would allow a hypothetical robot follower to track the same trajectory. Then, possible analytical relationships between F and V were obtained using neural network training. Results suggest that while F helps predict V, the relationship is not straightforward, that seemingly irrelevant components of F may be important, that force-velocity relationships are unique to each human follower, and that human neural control of movement may affect the prediction of the movement intent. It is suggested that user-specific, stereotype-free controllers may more accurately decode human intent in pHRI.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
