On The Combination of Gamification and Crowd Computation in Industrial Automation and Robotics Applications

Bewley, Tom; Liarokapis, Minas

doi:10.1109/icra.2019.8794040

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…For example, Dey and Sarkar [11] address "maintenance [...][as] a crucial aspect to prevent untimely breakdown as it might lead to decrease in productivity and huge financial losses". Bewley and Liarokapis [36] mention gamification as another way to reduce costs. Another important optimization with 27 mentions is the handling of information.…”

Section: Discussionmentioning

confidence: 99%

Cognitive Production Systems: A Mapping Study

Deutschmann¹,

Ghofrani²,

Reichelt³

2020

Preprint

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Production plants today are becoming more and more complicated through more automation and networking. It is becoming more difficult for humans to participate, due to higher speed and decreasing reaction time of these plants. Tendencies to improve production systems with the help of cognitive systems can be identified. The goal is to save resources and time. This mapping study gives an insight into the domain, categorizes different approaches and estimates their progress. Furthermore, it shows achieved optimizations and persisting problems and barriers. These representations should make it easier in the future to address concrete problems in this research field. Human-Machine Interaction and Knowledge Gaining/Sharing represent the largest categories of the domain. Most often, a gain in efficiency and maximized effectiveness can be achieved as optimization. The most common problem is the missing or only difficult generalization of the presented concepts.

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Section: Discussionmentioning

confidence: 99%

Cognitive Production Systems: A Mapping Study

Deutschmann¹,

Ghofrani²,

Reichelt³

2020

Preprint

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“…Our previous work (Bewley and Liarokapis, 2019 ), proposes abstract foundations for a framework combining gamification and crowdsourcing in a synergistic manner for robotics applications. The paper also introduces a standardized terminology for describing crowdsourcing techniques in robotics.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Human Perception in Robot Grasping and Manipulation Through Crowdsourcing and Gamification

2021

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Robot grasping in unstructured and dynamic environments is heavily dependent on the object attributes. Although Deep Learning approaches have delivered exceptional performance in robot perception, human perception and reasoning are still superior in processing novel object classes. Furthermore, training such models requires large, difficult to obtain datasets. This work combines crowdsourcing and gamification to leverage human intelligence, enhancing the object recognition and attribute estimation processes of robot grasping. The framework employs an attribute matching system that encodes visual information into an online puzzle game, utilizing the collective intelligence of players to expand the attribute database and react to real-time perception conflicts. The framework is deployed and evaluated in two proof-of-concept applications: enhancing the control of a robotic exoskeleton glove and improving object identification for autonomous robot grasping. In addition, a model for estimating the framework response time is proposed. The obtained results demonstrate that the framework is capable of rapid adaptation to novel object classes, based purely on visual information and human experience.

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