Supporting task-oriented collaboration in human-robot teams using semantic-based path planning

Yi, Daqing; Goodrich, Michael A.

doi:10.1117/12.2050718

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Research into collaboration in HRTs refers to linkages explicitly established in an HRT context, which should not be confused with the broader topic of HRC. Conceptual studies range in focus, including the optimal setup of “hybrid teams” with robots, virtual agents, and humans as team members (Schwartz et al, 2016), collaboration challenges (Fiore et al, 2011), collaborative tools (Bruemmer & Walton, 2003), the development of collaborative robotic teammates (Hayes & Scassellati, 2014), dynamic peer-to-peer teaming (Tang & Parker, 2006), task-oriented collaboration with semantic-based path planning (Yi & Goodrich, 2014), decision-making (Stewart et al, 2012), and mutual initiatives (Bruemmer et al, 2002). Researchers also examined collaboration frameworks (e.g., Hoffman & Breazeal, 2004; Marble et al, 2003) for dyadic HRTs and a framework of joint action perception (Iqbal et al, 2015) for multiple-member HRT.…”

Section: Conceptual and Empirical Findings Related To Human–robot Teamsmentioning

confidence: 99%

How and When Can Robots Be Team Members? Three Decades of Research on Human–Robot Teams

Wolf

Stock‐Homburg

2022

Group & Organization Management

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Artificial intelligence and robotic technologies have grown in sophistication and reach. Accordingly, research into mixed human–robot teams that comprise both robots and humans has expanded as well, attracting the attention of researchers from different disciplines, such as organizational behavior, human–robot interaction, cognitive science, and robotics. With this systematic literature review, the authors seek to establish deeper insights into existing research and sharpen the definitions of relevant terms. With a close consideration of 150 studies published between 1990 and 2020 that investigate mixed human–robot teams, conceptually or empirically, this article provides both a systematic evaluation of extant research and propositions for further research.

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Section: Conceptual and Empirical Findings Related To Human–robot Teamsmentioning

confidence: 99%

How and When Can Robots Be Team Members? Three Decades of Research on Human–Robot Teams

Wolf

Stock‐Homburg

2022

Group & Organization Management

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“…The problem with gesture identification in the context of autonomous vehicles is that human gestures are not easily understood by machines and gestures require a large library to have an accuracy that makes them meaningful. There have also been other approaches to the communication problem as seen in [27], [17], [34]. With audio feedback, [14], [4] accounts for the way that people perceive sounds coming from a machine, but a problem with this is that the sounds have to be taken in a specific context.…”

Section: Related Workmentioning

confidence: 99%

Intent Communication between Autonomous Vehicles and Pedestrians

Matthews¹,

Chowdhary²,

Kieson³

2017

Preprint

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When pedestrians encounter vehicles, they typically stop and wait for a signal from the driver to either cross or wait. What happens when the car is autonomous and there isn't a human driver to signal them? This paper seeks to address this issue with an intent communication system (ICS) that acts in place of a human driver. This intent system has been developed to take into account the psychology behind what pedestrians are familiar with and what they expect from machines. The system integrates those expectations into the design of physical systems and mathematical algorithms. The goal of the system is to ensure that communication is simple, yet effective without leaving pedestrians with a sense of distrust in autonomous vehicles. To validate the ICS, two types of experiments have been run: field tests with an autonomous vehicle to determine how humans actually interact with the ICS and simulations to account for multiple potential behaviors.The results from both experiments show that humans react positively and more predictably when the intent of the vehicle is communicated compared to when the intent of the vehicle is unknown. In particular, the results from the simulation specifically showed a 142 percent difference between the pedestrian's trust in the vehicle's actions when the ICS is enabled and the pedestrian has prior knowledge of the vehicle than when the ICS is not enabled and the pedestrian having no prior knowledge of the vehicle.

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“…Daqing Yi and Michael A. Goodric presented a new framework for sharing information between robots and humans for task-oriented collaboration [27]. In this work, they considered a cordon and search mission, which is one kind of military tactic for searching out the enemy in an area, as a human-robot collaborative task that has to be solved by a human-robot team.…”

Section: A Communicationmentioning

confidence: 99%

A survey of robot learning from demonstrations for Human-Robot Collaboration

Lee¹

2017

Preprint

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Robot learning from demonstration (LfD) is a research paradigm that can play an important role in addressing the issue of scaling up robot learning. Since this type of approach enables non-robotics experts can teach robots new knowledge without any professional background of mechanical engineering or computer programming skills, robots can appear in the real world even if it does not have any prior knowledge for any tasks like a new born baby. There is a growing body of literature that employ LfD approach for training robots. In this paper, I present a survey of recent research in this area while focusing on studies for human-robot collaborative tasks. Since there are different aspects between stand-alone tasks and collaborative tasks, researchers should consider these differences to design collaborative robots for more effective and natural human-robot collaboration (HRC). In this regard, many researchers have shown an increased interest in to make better communication framework between robots and humans because communication is a key issue to apply LfD paradigm for human-robot collaboration. I thus review some recent works that focus on designing better communication channels/methods at the first, then deal with another interesting research method, Interactive/Active learning, after that I finally present other recent approaches tackle a more challenging problem, learning of complex tasks, in the last of the paper.''Please pass me the trivet" ''Please pass me the trivet"

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Supporting task-oriented collaboration in human-robot teams using semantic-based path planning

Cited by 6 publications

References 2 publications

How and When Can Robots Be Team Members? Three Decades of Research on Human–Robot Teams

How and When Can Robots Be Team Members? Three Decades of Research on Human–Robot Teams

Intent Communication between Autonomous Vehicles and Pedestrians

A survey of robot learning from demonstrations for Human-Robot Collaboration

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