Legible action selection in human-robot collaboration

Zhu, Huaijiang; Gabler, Volker; Wollherr, Dirk

doi:10.1109/roman.2017.8172326

Cited by 7 publications

(9 citation statements)

References 17 publications

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“…By contrast, each subject experiences several experimental conditions in a randomized ordering in within-subject studies (e.g. [119], [197], [222], [223], [224], [225], [226], [227], [228]). Compared to between-subject designs, this approach enables comparisons between participants and allows the collection of more data per participant -this may, on the other hand, lead to habituation and fatigue effects [194].…”

Section: B Human-participant Studiesmentioning

confidence: 99%

Benchmarking Teamwork of Humans and Cobots—An Overview of Metrics, Strategies, and Tasks

2023

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Human-robot teaming receives an ever-increasing level of attention in research, development and industry. Novel approaches to task sharing in hybrid teams range from optimized schedules to intelligent cobot assistants with a high degree of autonomy. These approaches must prove their usefulness and benefits compared to manual work or full automation -particularly when it comes to assessing their potential for productive industrial use. This leverages demand for standardized, repeatable benchmarks to compare approaches and measure improvements in a reproducible way. Designing such benchmarks is challenging as numerous aspects, from safety considerations to human factors and team performance, must be considered. This survey seeks to contribute to the future development of benchmarks for the field of collaborative assembly, handling, and industrial cobot applications by giving a comprehensive overview of relevant metrics, evaluation strategies, and tasks for human-robot teams.

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Section: B Human-participant Studiesmentioning

confidence: 99%

Benchmarking Teamwork of Humans and Cobots—An Overview of Metrics, Strategies, and Tasks

2023

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“…the human's knowledge of the environment, plans, progress and goal, and triggers the cobot to only communicate with new information to the human when needed [93]. In tasks where several goals are possible, Zhu et al estimated the goal belief of the human and optimised their action sequence such that the wrong goal of the highest probability is eliminated [94]. Several other works studied the preference of humans for proactive (perform sub-tasks autonomously) versus reactive (perform tasks when triggered or asked for help) cobots [95,96].…”

Section: Kim Et Al [91] Peternel Et Al [7]mentioning

confidence: 99%

Cobot programming for collaborative industrial tasks: An overview

Zaatari

Marei

et al. 2019

Robotics and Autonomous Systems

332

172

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“…For example, predictable and legible motion strategies that indirectly communicate a robot's goals are readily interpreted by people (Dragan et al, 2013). These same strategies can be used in collaborative tasks to indirectly show the robot's goal to the user (Bodden et al, 2016;Faria et al, 2017;Zhu et al, 2017;Tabrez et al, 2019). Robots can also mimic human nonverbal behaviors like deictic eye gaze and pointing gestures to indicate task-relevant objects during collaborative tasks (Breazeal et al, 2004;Fischer et al, 2015) or to assist in completing mentally taxing tasks (Admoni et al, 2016;Hemminghaus and Kopp, 2017).…”

Section: Human Brainmentioning

confidence: 99%

Helping People Through Space and Time: Assistance as a Perspective on Human-Robot Interaction

2022

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As assistive robotics has expanded to many task domains, comparing assistive strategies among the varieties of research becomes increasingly difficult. To begin to unify the disparate domains into a more general theory of assistance, we present a definition of assistance, a survey of existing work, and three key design axes that occur in many domains and benefit from the examination of assistance as a whole. We first define an assistance perspective that focuses on understanding a robot that is in control of its actions but subordinate to a user’s goals. Next, we use this perspective to explore design axes that arise from the problem of assistance more generally and explore how these axes have comparable trade-offs across many domains. We investigate how the assistive robot handles other people in the interaction, how the robot design can operate in a variety of action spaces to enact similar goals, and how assistive robots can vary the timing of their actions relative to the user’s behavior. While these axes are by no means comprehensive, we propose them as useful tools for unifying assistance research across domains and as examples of how taking a broader perspective on assistance enables more cross-domain theorizing about assistance.

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Legible action selection in human-robot collaboration

Cited by 7 publications

References 17 publications

Benchmarking Teamwork of Humans and Cobots—An Overview of Metrics, Strategies, and Tasks

Benchmarking Teamwork of Humans and Cobots—An Overview of Metrics, Strategies, and Tasks

Cobot programming for collaborative industrial tasks: An overview

Helping People Through Space and Time: Assistance as a Perspective on Human-Robot Interaction

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