A playmate robot system for playing the rock-paper-scissors game with humans

Ahn, Ho Seok; Sa, Inkyu; Lee, Dong‐Wook; Choi, Dongwoon

doi:10.1007/s10015-011-0895-y

Cited by 25 publications

(9 citation statements)

References 6 publications

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“…This setting is expected to contribute to reduce the complexity of the prediction models. Humans make many different hand gestures with the result that it is difficult to understand them all [27]. This paper focuses on three typical gestures: Closed fist, flat hand, and scissor movement.…”

Section: Experimental Methodsmentioning

confidence: 99%

Gesture Prediction Using Wearable Sensing Systems with Neural Networks for Temporal Data Analysis

Kanokoda

Kushitani

Shimada

et al. 2019

Sensors

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A human gesture prediction system can be used to estimate human gestures in advance of the actual action to reduce delays in interactive systems. Hand gestures are particularly necessary for human–computer interaction. Therefore, the gesture prediction system must be able to capture hand movements that are both complex and quick. We have already reported a method that allows strain sensors and wearable devices to be fabricated in a simple and easy manner using pyrolytic graphite sheets (PGSs). The wearable electronics could detect various types of human gestures with high sensitivity, high durability, and fast response. In this study, we demonstrated hand gesture prediction by artificial neural networks (ANNs) using gesture data obtained from data gloves based on PGSs. Our experiments entailed measuring the hand gestures of subjects for learning purposes and we used these data to create four-layered ANNs, which enabled the proposed system to successfully predict hand gestures in real time. A comparison of the proposed method with other algorithms using temporal data analysis suggested that the hand gesture prediction system using ANNs would be able to forecast various types of hand gestures using resistance data obtained from wearable devices based on PGSs.

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Section: Experimental Methodsmentioning

confidence: 99%

Gesture Prediction Using Wearable Sensing Systems with Neural Networks for Temporal Data Analysis

Kanokoda

Kushitani

Shimada

et al. 2019

Sensors

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“…Subsequently, most works that introduced RPS robot designs focused on the creation of an interactive human robot hand which can form the three RPS hand shapes. Examples are the robot presented by Hasuda et al [18], the robot of Ahn et al [19] and the model by Lin et al [20]. The most recent prototype is the 'Janken robot' developed by the Ishikawa Watanabe laboratory [21].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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We present a novel implementation of a Rock-Paper-Scissors (RPS) game interaction with a social robot. The framework is tailored to be computationally lightweight, as well as entertaining and visually appealing through collaboration with designers and animators. The fundamental gesture recognition pipeline employs a Leap motion device and two separate machine learning architectures to evaluate kinematic hand data on-the-fly. The first architecture is used to recognize and segment human motion activity in order to initialize the RPS play, and the second architecture is used to classify hand gestures into rock, paper or scissors. The employed tabletop robot interacts in the RPS play through unique animated gestural movements and vocalizations designed by animators which communicate the robot's choices as well as cognitive reflection on winning, losing and draw states. Performance of both learning architectures is carefully evaluated with respect to accuracy, reliability and run time performance under different feature and classifier types. Moreover, we assess our system during an interactive RPS play between robot and human. Experimental results show that the proposed system is robust to user variations and play style in real environment conditions. As such, it offers a powerful application for the subsequent exploration of social human-machine interaction.

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“…In particular, service robots are becoming ever more commonplace in a number of daily life activities. Commercial products and research prototypes include robots used in home automation settings [2][3][4], entertainment [5,6], education [7,8], and elderly assistance applications [9,10], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Human-Robot Interfaces for Interactive Receptionist Systems and Wayfinding Applications

Bazzano¹,

Lamberti²

2018

Robotics

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Service robots are playing an increasingly relevant role in society. Humanoid robots, especially those equipped with social skills, could be used to address a number of people’s daily needs. Knowing how these robots are perceived and potentially accepted by ordinary users when used in common tasks and what the benefits brought are in terms, e.g., of tasks’ effectiveness, is becoming of primary importance. This paper specifically focuses on receptionist scenarios, which can be regarded as a good benchmark for social robotics applications given their implications on human-robot interaction. Precisely, the goal of this paper is to investigate how robots used as direction-giving systems can be perceived by human users and can impact on their wayfinding performance. A comparative analysis is performed, considering both solutions from the literature and new implementations which use different types of interfaces to ask for and give directions (voice, in-the-air arm pointing gestures, route tracing) and various embodiments (physical robot, virtual agent, interactive audio-map). Experimental results showed a marked preference for a physical robot-based system showing directions on a map over solutions using gestures, as well as a positive effect of embodiment and social behaviors. Moreover, in the comparison, physical robots were generally preferred to virtual agents.

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A playmate robot system for playing the rock-paper-scissors game with humans

Cited by 25 publications

References 6 publications

Gesture Prediction Using Wearable Sensing Systems with Neural Networks for Temporal Data Analysis

Gesture Prediction Using Wearable Sensing Systems with Neural Networks for Temporal Data Analysis

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

Human-Robot Interfaces for Interactive Receptionist Systems and Wayfinding Applications

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