Comparing alternative modalities in the context of multimodal human–robot interaction

Saren, Suprakas; Mukhopadhyay, Abhishek; Ghose, Debasish; Biswas, Pradipta

doi:10.1007/s12193-023-00421-w

Cited by 2 publications

(8 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As illustrated in Figure 5, among all the studies, most of the interaction type between humans and robots is collaboration (n = 34, 72.9%), the others are cooperation (n = 8, 16.7%), coexistence (n = 3, 6.2%), and teleoperation (n = 2, 4.2%). Figure 5 shows that the studies reviewed primarily focused on assembly tasks [38,39,48,53,54,57,58,61,63,64,72,73,76,77,80] with other frequent activities including pickand-place [44,55,60,65,79], screwing [41,70,71], handling [38,41], sorting [51,79], lifting [41,49], transferring [36,70], and surgical procedures [47,69]. These tasks represent the core applications of collaborative robots in current research.…”

Section: Types Of Interaction and Tasksmentioning

confidence: 99%

“…Other unique cobot types included a 4-DoF cobot, the surgical robot da Vinci, and virtual robots in simulated environments, indicating a diverse range of robotic platforms used in HRC research. Figure 5 shows that the studies reviewed primarily focused on assembly tasks [38,39,48,53,54,57,58,61,63,64,72,73,76,77,80] with other frequent activities including pick-and-place [44,55,60,65,79], screwing [41,70,71], handling [38,41], sorting [51,79], lifting [41,49], transferring [36,70], and surgical procedures [47,69]. These tasks represent the core applications of collaborative robots in current research.…”

Section: Types Of Cobotsmentioning

confidence: 99%

“…trust [17,45,48,57,73,77] physical attribute of the robot [39,70,72] user interface [37,38,42,46,51,60,63,64,73] dynamic of the situation [75] acceptance [17,57,71,77] robot errors [36,55] control modality [37,65,71] task complexity [36,55,66,69,80] human characteristics [39] trajectory and movement [53,55,78] feedback [39,48,51,52,62,72] design of workspace [39,73,77] physical workload [50] role of the robot [61,76,…”

Section: Human Factors Robot Factors Communication Factors Environmen...mentioning

confidence: 99%

“…Figure 7 shows that, among all selected studies, the Graphical User Interface (GUI) [37,39,46,54,57,60,63,66,69,[71][72][73]75,77,80] was the most utilized type of user interface, implemented in 15 studies. 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.…”

Section: User Interfacementioning

confidence: 99%

“…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%

See 4 more Smart Citations

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

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.

show abstract

Section: Types Of Interaction and Tasksmentioning

confidence: 99%

Section: Types Of Cobotsmentioning

confidence: 99%

Section: Human Factors Robot Factors Communication Factors Environmen...mentioning

confidence: 99%

Section: User Interfacementioning

confidence: 99%

Section: User Interfacementioning

confidence: 99%

See 3 more Smart Citations

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

Advanced Neural Classifier-Based Effective Human Assistance Robots Using Comparable Interactive Input Assessment Technique

Albekairi,

Kaaniche,

Abbas

et al. 2024

Mathematics

View full text Add to dashboard Cite

The role of robotic systems in human assistance is inevitable with the bots that assist with interactive and voice commands. For cooperative and precise assistance, the understandability of these bots needs better input analysis. This article introduces a Comparable Input Assessment Technique (CIAT) to improve the bot system’s understandability. This research introduces a novel approach for HRI that uses optimized algorithms for input detection, analysis, and response generation in conjunction with advanced neural classifiers. This approach employs deep learning models to enhance the accuracy of input identification and processing efficiency, in contrast to previous approaches that often depended on conventional detection techniques and basic analytical methods. Regardless of the input type, this technique defines cooperative control for assistance from previous histories. The inputs are cooperatively validated for the instruction responses for human assistance through defined classifications. For this purpose, a neural classifier is used; the maximum possibilities for assistance using self-detected instructions are recommended for the user. The neural classifier is divided into two categories according to its maximum comparable limits: precise instruction and least assessment inputs. For this purpose, the robot system is trained using previous histories and new assistance activities. The learning process performs comparable validations between detected and unrecognizable inputs with a classification that reduces understandability errors. Therefore, the proposed technique was found to reduce response time by 6.81%, improve input detection by 8.73%, and provide assistance by 12.23% under varying inputs.

show abstract

Comparing alternative modalities in the context of multimodal human–robot interaction

Cited by 2 publications

References 48 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

Advanced Neural Classifier-Based Effective Human Assistance Robots Using Comparable Interactive Input Assessment Technique

Contact Info

Product

Resources

About