Modeling Player Activity in a Physical Interactive Robot Game Scenario

Oliveira, Ewerton Lopes Silva de; Orrù, Davide; Nascimento, Tiago P.; Bonarini, Andrea

doi:10.1145/3125739.3132608

Cited by 6 publications

(8 citation statements)

References 4 publications

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“…Accelerometers can also be used to interpret gestures or human activities. For instance, in [67], a human player involved in a robogame wears an accelerometer that gives implicitly to the opponent robot information about the type and the quality of activity that the person is performing, thus allowing the robot to adapt its playing style to the human player.…”

Section: The Touch Channelmentioning

confidence: 99%

Communication in Human-Robot Interaction

Bonarini

2020

Curr Robot Rep

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Purpose of Review To present the multi-faceted aspects of communication between robot and humans (HRI), putting in evidence that it is not limited to language-based interaction, but it includes all aspects that are relevant in communication among physical beings, exploiting all the available sensor channels. Recent Findings For specific purposes, machine learning algorithms could be exploited when data sets and appropriate algorithms are available. Summary Together with linguistic aspects, physical aspects play an important role in HRI and make the difference with respect to the more limited human-computer interaction (HCI). A review of the recent literature about the exploitation of different interaction channels is presented. The interpretation of signals and the production of appropriate communication actions require to consider psychological, sociological, and practical aspects, which may affect the performance. Communication is just one of the functionalities of an interactive robot and, as all the others, will need to be benchmarked to support the possibility for social robots to reach a real market.

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Section: The Touch Channelmentioning

confidence: 99%

Communication in Human-Robot Interaction

Bonarini

2020

Curr Robot Rep

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“…RoboTower [31][32][33], is a game where human players physically interact with an omni-directional robot by trying to conquer towers while preventing the robot to push them down. A player model is maintained and used to adapt the game to the performance of the player.…”

Section: Physically Interactive Robogamesmentioning

confidence: 99%

Deceptive Actions to Improve the Attribution of Rationality to Playing Robotic Agents

Oliveira

Donadoni

Boriero

et al. 2020

Int J of Soc Robotics

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Play is a common activity, providing not only pleasure but also physical and cognitive development. In the quest for new playing experiences, there is an increasing tendency to develop robots playing with people. Making believable playing robots able to keep human players engaged and satisfied by the playing experience is the main challenge.In this work, we investigate the possibilities of a playful interaction between a human player and a mobile robot. In particular, this paper focuses on the applicability of deception as a means to support engagement and the attribution of rationality to playing robotic agents. By analyzing the interaction situation between the human and robot players, by identifying the need for deception, and by deciding whether and how to deceive, we aim at increasing self-reported engagement and fun, which are also related to the perception of the robotic opponent as smart enough to compete at an appropriate level.Experiments were conducted on a sample of 78 subjects facing two different deceptive behaviors and a basic behavior without any deception. All participants responded to a post-interaction questionnaire from which it was possible to observe a positive acceptance of the perception of the robot as a rational agent aimed at winning. In general, deception was perceived by most of the players as one of the robot's abilities, when actuated, and contributed to the reported fun.

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“…Using the dataset from previous work [13,14], we considered 29 matches involving 15 male participants of different ages. The age distribution consisted of children (7-10) and adults (26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40).…”

Section: Game Scenariomentioning

confidence: 99%

“…A different and more complex version of RoboTower has been recently implemented [13,14], where humans interact with a three-wheeled omni-directional robot one meter high. In this version, only physical interaction is exploited.…”

Section: Introductionmentioning

confidence: 99%

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Learning and Mining Player Motion Profiles in Physically Interactive Robogames

et al. 2018

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Physically-Interactive RoboGames (PIRG) are an emerging application whose aim is to develop robotic agents able to interact and engage humans in a game situation. In this framework, learning a model of players' activity is relevant both to understand their engagement, as well as to understand specific strategies they adopted, which in turn can foster game adaptation. Following such directions and given the lack of quantitative methods for player modeling in PIRG, we propose a methodology for representing players as a mixture of existing player's types uncovered from data. This is done by dealing both with the intrinsic uncertainty associated with the setting and with the agent necessity to act in real time to support the game interaction. Our methodology first focuses on encoding time series data generated from player-robot interaction into images, in particular Gramian angular field images, to represent continuous data. To these, we apply latent Dirichlet allocation to summarize the player's motion style as a probabilistic mixture of different styles discovered from data. This approach has been tested in a dataset collected from a real, physical robot game, where activity patterns are extracted by using a custom three-axis accelerometer sensor module. The obtained results suggest that the proposed system is able to provide a robust description for the player interaction.

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Modeling Player Activity in a Physical Interactive Robot Game Scenario

Cited by 6 publications

References 4 publications

Communication in Human-Robot Interaction

Communication in Human-Robot Interaction

Deceptive Actions to Improve the Attribution of Rationality to Playing Robotic Agents

Learning and Mining Player Motion Profiles in Physically Interactive Robogames

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