Effects of nonverbal communication on efficiency and robustness in human-robot teamwork

By Cynthia Lynn Breazeal, Cory D. Kidd, Andrea Lockerd Thomaz, Guy Hoffman, Matt Berlin. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005

653 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Social Psychology

View Paper: https://www.semanticscholar.org/paper/4c3d032648939b63bcca1794119115d623fdc376

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Citations in the corpus

A review of verbal and non-verbal human-robot interactive communication (2014, background): “Eye gaze cues are important for coordinating collaborative tasks [141], [142], and also, eye gazes are an important subset of non-verbal communication cues that can increase efﬁciency and robustness in human-robot teamwork [143].”
Effects of anticipatory action on human-robot teamwork: Efficiency, fluency, and perception of team (2007, background): “We have previously presented work in shared-location humanrobot teamwork, investigating the role of nonverbal behavior on teamwork [11, 4].”
Cost-Based Anticipatory Action Selection for Human–Robot Fluency (2007, background): “We have previously presented work in shared-location human–robot teamwork, investigating the role of nonverbal behavior on teamwork [7], [20].”
How Robot Verbal Feedback Can Improve Team Performance in Human-Robot Task Collaborations (2015, background): “Other relevant work demonstrated robot learning of simple tasks through human tutelage and effective collaboration via turn-taking or biased pre-emptive action [1, 8].”
A survey of robot learning from demonstrations for Human-Robot Collaboration (2017, background): “Breazeal et al. showed that a robot’s non-verbal cues are important for building teamwork between humans and robots [28].”
Wizard of Oz studies in HRI (2012, methodology): “From an engineering perspective, Breazeal et al. (2005) suggest that relying on WoZ as an experimental technique can make it all the more difficult to build robots capable of successfully mitigating errors on their own in the future (Breazeal, Kidd, Thomaz, Hoffman, & Berlin, 2005).”
Implicit Communication of Actionable Information in Human-AI teams (2019, background): “In order to be effective partners and integrate well with human teams, robots and AIs need to understand and generate this kind of actionable implicit communication that results from actions being situated in context [9, 18, 27].”
Expressing thought: Improving robot readability with animation principles (2011, background): “, offices, homes, airports, marketplaces) can be safer and more effective at performing work if they are designed with similarly humanreadable behaviors [4].”
Using perspective taking to learn from ambiguous demonstrations (2006, methodology): “In order to perceive the upper torso pose of a human interacting with the robot, Leo uses the VTracker articulated body tracking software developed and generously provided for our use by the Vision Interfaces group at the MIT Computer Science and Artificial Intelligence Laboratory.”
Designing persuasive robots: How robots might persuade people using vocal and nonverbal cues (2012, background): “, [47, 5, 25]), the way in which these cues might shape the persuasive ability of a robot has not yet been studied.”