Activity recognition in a physical interactive robogame

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

doi:10.1109/devlrn.2017.8329793

Cited by 5 publications

(7 citation statements)

References 14 publications

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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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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

Self Cite

View full text Add to dashboard Cite

show abstract

“…The choice of the accelerometer position was conditioned by the need to avoid the introduction of noise from irrelevant player motion. With the sensor placed on the chest, we aimed at getting a more stable reference for motion in comparison to, for instance, data gathered from the sensor placed on the arm or thigh [13]. The custom accelerometer module is based on the InvenSense MPU-6050 three-axis accelerometer board and an Arduino Uno micro-controller (see Figure 12).…”

Section: Game Scenariomentioning

confidence: 99%

“…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%

“…The window size should be large enough to capture motion patterns relevant for the player characterization, but not so large to confound their interpretation. We tried different window sizes, and in the end, we followed [13] and decided to consider half a second of data per window without overlap, which allows for a fine-grained data representation.…”

Section: Feature Extractionmentioning

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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Influence of Reaction Time in the Emotional Response of a Companion Robot to a Child’s Aggressive Interaction

Alhaddad

Cabibihan

Bonarini

2020

Int J of Soc Robotics

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The quality of a companion robot’s reaction is important to make it acceptable to the users and to sustain interactions. Furthermore, the robot’s reaction can be used to train socially acceptable behaviors and to develop certain skills in both normally developing children and children with cognitive disabilities. In this study, we investigate the influence of reaction time in the emotional response of a robot when children display aggressive interactions toward it. Different interactions were considered, namely, pickup, shake, drop and throw. The robot produced responses as audible sounds, which were activated at three different reaction times, namely, 0.5 s, 1.0 s, and 1.5 s. The results for one of the tasks that involved shaking the robotic toys produced a significant difference between the timings tested. This could imply that producing a late response to an action (i.e. greater than 1.0 s) could negatively affect the children’s comprehension of the intended message. Furthermore, the response should be comprehensible to provide a clear message to the user. The results imply that the designers of companion robotic toys need to consider an appropriate timing and clear modality for their robots’ responses.

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Activity recognition in a physical interactive robogame

Cited by 5 publications

References 14 publications

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

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

Learning and Mining Player Motion Profiles in Physically Interactive Robogames

Influence of Reaction Time in the Emotional Response of a Companion Robot to a Child’s Aggressive Interaction

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