Improved human-robot team performance using Chaski, A human-inspired plan execution system

By Julie Arnold Shah, James Wiken, Brian Charles Williams, Cynthia Lynn Breazeal. 2011

253 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Social Psychology

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Citations in the corpus

Plan explicability and predictability for robot task planning (2015, background): “ legibility there is analogous to predictability in ours. In the human-robot interaction (HRI) community, there exists prior works that discuss how to enable natural and ﬂuent human-robot interaction [11, 12, 21, 18] to create more socially acceptable robots [7]. These works, however, apply only to ”
An implemented theory of mind to improve human-robot shared plans execution (2016, background): “present Chaski, a task-level executive that allows to choose when to execute the robot actions adapting to the human partner [23].”
Human–Robot Interaction (2016, unknown): “With the aim of improving human–robot side-by-side interactions in industrial settings, such as aircraft assembly, Shah, Wiken, Williams, and Breazeal (2011) and Gombolay, Huang, and Shah (2015) have demonstrated techniques for observing human subjects in performing a manipulation task.”
Autonomously constructing hierarchical task networks for planning and human-robot collaboration (2016, background): “This is known as leastcommitment planning, an important concept in multi-agent domains with uncertainty [6].”
Challenges in Shared-Environment Human-Robot Collaboration (2013, background): “Important work yet to be addressed within this space includes the handling of unfulfilled commitments by teammates and handling execution-time variation in ordering constraints [28], [29].”
How Robot Verbal Feedback Can Improve Team Performance in Human-Robot Task Collaborations (2015, background): “This prior work has demonstrated that robots that account for the actions of their collaborators when deciding what to do are preferred and perceived as more intelligent [17] and that anticipatory action can play an important role in increasing team fluency [8].”
Improved human–robot team performance through cross-training, an approach inspired by human team training practices (2015, result): “Furthermore, cross-training had a positive impact on their degree of trust in Abbie, in accordance with prior work (Shah et al., 2011).”
The future of skills: employment in 2030 (2017, background): “Just as significantly they do not assess the potential for job creation in tasks and occupations complemented by automation or the adjustments that are triggered in other parts of the economy through relative wage changes and other market forces (Shah et al., 2011; Davenport and Kirby, 2016; Kasparo”
Algorithms at Work: The New Contested Terrain of Control (2020, background): “They argue that these benefits to organizations will unfold in automated and tightly coupled feedback loops between user and system behavior (e.g., Nikolaidis & Shah, 2012; Sachon & Boquet, 2017; Shah, Wiken, Williams, & Breazeal, 2011).”
Human-robot cross-training: Computational formulation, modeling and evaluation of a human team training strategy (2013, methodology): “2 in Section III-B) for comparison purposes.”
Human-robot collaborative manipulation planning using early prediction of human motion (2013, background): “To account for varying action costs at the process level, we seek to explore integration with task-level planning for human-robot teams such as [27].”
Evaluating Fluency in Human–Robot Collaboration (2019, background): “Fluency in human–robot collaboration has garnered interest over the past several years, as a large portion of the human–robot interaction (HRI) literature is working toward computational models of collaboration (e.g., [1]–[3]; for a survey, see [4]).”