Developing a Lightweight Rock-Paper-Scissors Framework for Human-Robot Collaborative Gaming

Brock, Heike; Ponce-Chulani, Javier; Merino, Luís; Szapiro, Deborah; Gómez, Randy

doi:10.1109/access.2020.3033550

Cited by 15 publications

(3 citation statements)

References 27 publications

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“…The RPS game described in this paper is heavily reliant on hardware and a lack of movement-related design concerns. All of the essential motion perception is performed by a single Leap Motion device, which is connected to the basic robot hardware to provide mobility [5]. Consensus algorithms for Block chain are required to ensure that the system is fully decentralized.…”

Section: Related Workmentioning

confidence: 99%

Rock, Paper and Scissor Using AI- Random Forest Algorithm

Murukesh

2023

IoT, Cloud and Data Science

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Rock, paper, scissors (RPS) is a game of crucial decision-making that can be used to test various learning algorithms due to its simplicity. Random Forest is a Rock Paper Scissors Agent that makes predictions using the Random Forest Classification with a bit of random. The randomness on this agent has been limited only while losing (and partially due to window length), displaying the algorithm's true potential. A simulation of various AI agents is developed to compare and analyze against the Random forest algorithm.

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Section: Related Workmentioning

confidence: 99%

Rock, Paper and Scissor Using AI- Random Forest Algorithm

Murukesh

2023

IoT, Cloud and Data Science

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“…Application of gesture recognition involves virtual reality [121], augmented reality [122], bio medics [123], and robotics [37]. Robot control based on gesture recognition have been investigated in [37], [124]- [126]. The movement of robot arm manipulator SCORBOT-ER 9 Pro was controlled in [37] by detecting human body landmarks.…”

Section: Introductionmentioning

confidence: 99%

Towards Controlling Mobile Robot Using Upper Human Body Gesture Based on Convolutional Neural Network

Fuad,

Umam,

Wahyuni

et al. 2023

Journal of Robotics and Control

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Human-Robot Interaction (HRI) has challenges in investigation of a nonverbal and natural interaction. This study contributes to developing a gesture recognition system capable of recognizing the entire human upper body for HRI, which has never been done in previous research. Preprocessing is applied to improve image quality, reduce noise and highlight important features of each image, including color segmentation, thresholding and resizing. The hue, saturation, value (HSV) color segmentation is executed by utilizing blue color backdrop and additional lighting to deal with illumination issue. Then thresholding is performed to get a black and white image to distinguish between background and foreground. The resizing is completed to adjust the image to match the size expected by the model. The preprocessed data image is used as input for gesture recognition based on Convolutional Neural Network (CNN). This study recorded five gestures from five research subjects in difference gender and body posture with total of 450 images which divided into 380 and 70 images for training and testing respectively. Experiments that performed in an indoor environment showed that CNN achieved 92% of accuracy in the gesture recognition. It has lower level of accuracy compare to AlexNet model but with faster training computation time of 9 seconds. This result was obtained by testing the system over various distances. The optimal distance for a camera setting from user to interact with mobile robot by using gesture was 2.5 m. For future research, the proposed method will be improved and implemented for mobile robot motion control.

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“…The framework used manual motion to drive the tooltip, a 3D camera–based method to adjust the workspace, calculation of optimal instrument orientation, and cartesian interpolation to assure safety. In other studies, researchers proposed frameworks for different human-robot collaboration–based applications such as industrial cyber-physical systems [ 30 ], interaction in games (ie, Rock-Paper-Scissors) [ 31 ], and cooperative assembly duties [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Mixed Reality–Based Control of Collaborative Robot: Development Study

Shahria¹,

Sunny²,

Zarif³

et al. 2022

JMIR Biomed Eng

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Background Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research. Objective This study aims to present the development of a novel framework for the collaborative robot using mixed reality. Methods The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. It also uses the Windows Mixed Reality platform to show digital materials on holographic display and the Azure mixed reality services to capture and expose digital information. Eventually, it uses a holographic device (HoloLens 2) to execute the mixed reality–based collaborative system. Results A thorough experiment was conducted to validate the novel framework for mixed reality–based control of a collaborative robot. This framework was successfully applied to implement a collaborative system using a 5–degree of freedom robot (xArm-5) in a mixed reality environment. The framework was stable and worked smoothly throughout the collaborative session. Due to the distributed nature of cloud applications, there is a negligible latency between giving a command and the execution of the physical collaborative robot. Conclusions Opportunities for collaborative robots in telerehabilitation and teleoperation are vital as in any other field. The proposed framework was successfully applied in a collaborative session, and it can also be applied in other similar potential applications for robust and more promising performance.

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Developing a Lightweight Rock-Paper-Scissors Framework for Human-Robot Collaborative Gaming

Cited by 15 publications

References 27 publications

Rock, Paper and Scissor Using AI- Random Forest Algorithm

Rock, Paper and Scissor Using AI- Random Forest Algorithm

Towards Controlling Mobile Robot Using Upper Human Body Gesture Based on Convolutional Neural Network

A Novel Framework for Mixed Reality–Based Control of Collaborative Robot: Development Study

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