Examining the Effects of Anticipatory Robot Assistance on Human Decision Making

Newman, Benjamin A.; Biswas, Abhijat; Ahuja, Sarthak; Girdhar, Siddharth; Admoni, Henny

doi:10.1007/978-3-030-62056-1_49

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Other interfaces include physical interaction [49,57,69,[71][72][73]77] and touch interfaces [46,55,65,71], which facilitate direct engagement with robots. Additional methods such as gesture recognition [38,39,63,65,72,73], voice control [36,38,[63][64][65], and eye gaze tracking [65,68] were employed, enhancing the versatility of interactions. Haptic feedback [62] and extended reality (XR) interfaces [38,39,46,52,59,60,63] were noted for their immersive and tactile capabilities.…”

Section: User Interfacementioning

confidence: 99%

“…Other technologies such as Brain-Computer Interface (BCI) [40], eye gaze [65,68], and AI-based perception [57,72] were also mentioned. Such technologies have the potential to reduce cognitive workload and enhance users' preference and needs therefore improving their decision making.…”

Section: Technologies Related To Human Decision Makingmentioning

confidence: 99%

“…Human-centred design is also discussed and suggested. For example, considerations for a more human-centred HRC design [17]; understanding the bidirectional nature of anticipatory actions and improving the accuracy of intent prediction [68]; using simulations to predict and plan for human behaviour and developing tools for coupling human models [50]; designing with consideration for operator states, appropriate task allocation, and the use of adaptive interfaces [45]; and designing collaborative system features that can adapt to individual user preferences and needs [72].…”

Section: Potential Solutionsmentioning

confidence: 99%

See 2 more Smart Citations

What Affects Human Decision Making in Human–Robot Collaboration?: A Scoping Review

Liu,

Caldwell,

Rittenbruch

et al. 2024

Robotics

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The advent of Industry 4.0 has heralded advancements in Human–robot Collaboration (HRC), necessitating a deeper understanding of the factors influencing human decision making within this domain. This scoping review examines the breadth of research conducted on HRC, with a particular focus on identifying factors that affect human decision making during collaborative tasks and finding potential solutions to improve human decision making. We conducted a comprehensive search across databases including Scopus, IEEE Xplore and ACM Digital Library, employing a snowballing technique to ensure the inclusion of all pertinent studies, and adopting the PRISMA Extension for Scoping Reviews (PRISMA-ScR) for the reviewing process. Some of the important aspects were identified: (i) studies’ design and setting; (ii) types of human–robot interaction, types of cobots and types of tasks; (iii) factors related to human decision making; and (iv) types of user interfaces for human–robot interaction. Results indicate that cognitive workload and user interface are key in influencing decision making in HRC. Future research should consider social dynamics and psychological safety, use mixed methods for deeper insights and consider diverse cobots and tasks to expand decision-making studies. Emerging XR technologies offer the potential to enhance interaction and thus improve decision making, underscoring the need for intuitive communication and human-centred design.

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Section: User Interfacementioning

confidence: 99%

Section: Technologies Related To Human Decision Makingmentioning

confidence: 99%

Section: Potential Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

What Affects Human Decision Making in Human–Robot Collaboration?: A Scoping Review

Liu,

Caldwell,

Rittenbruch

et al. 2024

Robotics

View full text Add to dashboard Cite

show abstract

“…A direct approach can be having users annotate the robots' sensor input (e.g., camera streams) [51,60], but this usually requires an additional device. Tracking the human's gaze enables anticipatory robot planning and execution of actions [2,4,5,25,40], or can be used to prompt robot takeover when it detects user hesitation [47]. A gesture is another mechanism to direct attention by humans and robots [49], including pointing, presenting, and exhibiting objects while collaborating on physical tasks [20].…”

Section: Human Cues For Human-robot Interactionmentioning

confidence: 99%

ASTEROIDS: Exploring Swarms of Mini-Telepresence Robots for Physical Skill Demonstration

Sousa

Chu

et al. 2022

CHI Conference on Human Factors in Computing Systems

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People use how-to videos, both live and pre-recorded, to learn new physical skills [37], ranging from repairing a broken keyboard to learning digital fabrication [42]. In most how-to videos for physical skills, the instructor demonstrates step-by-step how to complete the task [14]. As these steps may involve activities at varying locations in varying levels of detail, a single, fixed camera often cannot record every step with the desired clarity [39]. This necessitates frequent changes to camera parameters, including viewpoints, angles, and zoom levels.Professional video productions, such as cooking and home improvement shows, employ several dedicated camera operators who actively re-position cameras and adjust their parameters in response to the instructor's actions. However, such resources are not available to most instructors; instead, these rely on one or more preconfigured fixed cameras. Although fixed camera setups can be re-configured during recording, instructors need to stop what they are demonstrating (e.g., chopping vegetables) to manipulate the camera. This disrupts the demonstration, increasing the instructor's workload. It also requires more post-processing to combine clips filmed with different camera setups.Both filmmakers and researchers have explored the idea of cameramanipulating robots as an alternative to human operators [1,27]. Recent camera robots (predominantly drones) can autonomously track moving subjects [6,24,41]. However, it remains a challenge for the user being filmed to control the camera robots' behaviors while performing other activities, such as demonstrating a physical process. Conventional interfaces for robot control employ joysticks [52], gestures [49], and speech [16]-all of which require dedicated input actions-that disrupt instruction delivery. If not edited out, such disruptions might split audience's attention and hinder learning [9], but post-processing adds to instructors' efforts. Recent user interface research has explored triggering on-screen visual effects through presenters' gestures and speech [22,32,48] that are part of the presentations. Our approach in this work is to:(1) identify the kinds of camera shots that how-to videos use and (2) direct camera operations in a non-disruptive manner by relying on the communicative signals that instructors already use to address their audience during demonstrations. For instance, an instructor may point to a part of an object to emphasize it, use speech to guide the audience's attention, or wave to introduce themselves.

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Modeling of anticipation using instance-based learning: application to automation surprise in aviation using passive BCI and eye-tracking data

Klaproth,

Dietz,

Pawlitzki

et al. 2024

User Model User-Adap Inter

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Examining the Effects of Anticipatory Robot Assistance on Human Decision Making

Cited by 8 publications

References 19 publications

What Affects Human Decision Making in Human–Robot Collaboration?: A Scoping Review

What Affects Human Decision Making in Human–Robot Collaboration?: A Scoping Review

ASTEROIDS: Exploring Swarms of Mini-Telepresence Robots for Physical Skill Demonstration

Modeling of anticipation using instance-based learning: application to automation surprise in aviation using passive BCI and eye-tracking data

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