The Role of Functional Affordances in Socializing Robots

Awaad, Iman; Kraetzschmar, Gerhard K.; Hertzberg, Joachim

doi:10.1007/s12369-015-0281-3

Cited by 28 publications

(25 citation statements)

References 32 publications

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“…KnowRob has multiple knowledge representation and reasoning capabilities and has been successfully deployed in complex tasks, such as identifying missing items on a table [38], operating containers [39], multi-robot coordination [40], and semantic mapping [41]. Non-monotonic knowledge representation and reasoning systems are typically based on Answer Set Programming (ASP) (e.g., References [29,42,43]) and extensions of OWL-DL that allow the use of incomplete information have been defined (e.g., References [44]), some of which have been demonstrated in different complex tasks [42,[44][45][46][47][48]. Awaad et al [44] use OWL-DL to model preferences and functional affordances for establishing social-accepted behaviors and guidelines to improve human-robot interaction and carrying out tasks in real-world scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…Non-monotonic knowledge representation and reasoning systems are typically based on Answer Set Programming (ASP) (e.g., References [29,42,43]) and extensions of OWL-DL that allow the use of incomplete information have been defined (e.g., References [44]), some of which have been demonstrated in different complex tasks [42,[44][45][46][47][48]. Awaad et al [44] use OWL-DL to model preferences and functional affordances for establishing social-accepted behaviors and guidelines to improve human-robot interaction and carrying out tasks in real-world scenarios. In addition, an application of dynamic knowledge-acquisition through the interaction with a teacher is provided by Berlin et al [49].…”

Section: Related Workmentioning

confidence: 99%

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Deliberative and Conceptual Inference in Service Robots

et al. 2021

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Service robots need to reason to support people in daily life situations. Reasoning is an expensive resource that should be used on demand whenever the expectations of the robot do not match the situation of the world and the execution of the task is broken down; in such scenarios, the robot must perform the common sense daily life inference cycle consisting on diagnosing what happened, deciding what to do about it, and inducing and executing a plan, recurring in such behavior until the service task can be resumed. Here, we examine two strategies to implement this cycle: (1) a pipe-line strategy involving abduction, decision-making, and planning, which we call deliberative inference and (2) the use of the knowledge and preferences stored in the robot’s knowledge-base, which we call conceptual inference. The former involves an explicit definition of a problem-space that is explored through heuristic search, and the latter is based on conceptual knowledge, including the human user preferences, and its representation requires a non-monotonic knowledge-based system. We compare the strengths and limitations of both approaches. We also describe a service robot conceptual model and architecture capable of supporting the daily life inference cycle during the execution of a robotics service task. The model is centered in the declarative specification and interpretation of robot’s communication and task structure. We also show the implementation of this framework in the fully autonomous robot Golem-III. The framework is illustrated with two demonstration scenarios.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deliberative and Conceptual Inference in Service Robots

et al. 2021

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“…They explained that this, much richer level of affordance representation is needed to allow artificial agents to be adaptable to novel open-world scenarios. Reference [63] also underlined the fact that affordances play an important role on basic cognitive capabilities such as prediction and planning. The authors said that the problem of learning affordances is a key step toward understanding the world properties and developing social skills.…”

Section: Experimental Work For Cognitive Architecturesmentioning

confidence: 99%

“…Affordances [41,62,63] Affordances are important elements to be analyzed in the process of the implementation of a behavioral model for a cognitive robot…”

mentioning

confidence: 99%

A Survey of Behavioral Models for Social Robots

et al. 2019

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The cooperation between humans and robots is becoming increasingly important in our society. Consequently, there is a growing interest in the development of models that can enhance and enrich the interaction between humans and robots. A key challenge in the Human-Robot Interaction (HRI) field is to provide robots with cognitive and affective capabilities, by developing architectures that let them establish empathetic relationships with users. Over the last several years, multiple models were proposed to face this open-challenge. This work provides a survey of the most relevant attempts/works. In details, it offers an overview of the architectures present in literature focusing on three specific aspects of HRI: the development of adaptive behavioral models, the design of cognitive architectures, and the ability to establish empathy with the user. The research was conducted within two databases: Scopus and Web of Science. Accurate exclusion criteria were applied to screen the 4916 articles found. At the end, 56 articles were selected. For each work, an evaluation of the model is made. Pros and cons of each work are detailed by analyzing the aspects that can be improved to establish an enjoyable interaction between robots and users.

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“…There are many factors affecting how we perceive robots, from physical organization and appearance, to more subtle influences based on function and perceived intent. Functional affordances of a robot are the actions it is able to do, be it physical actions, gestures, or utterances (Awaad et al, 2015). In a study examining responses to multiple kinds of social robots, participants were more likely to report stronger engagement with a robot and intention to use it if it had sufficient affordances to complete a physical task, while physical appearance was rated as less important for engagement (Paauwe et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Sensorimotor Oscillations During a Reciprocal Touch Paradigm With a Human or Robot Partner

2018

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Robots provide an opportunity to extend research on the cognitive, perceptual, and neural processes involved in social interaction. This study examined how sensorimotor oscillatory electroencephalogram (EEG) activity can be influenced by the perceived nature of a task partner – human or robot – during a novel “reciprocal touch” paradigm. Twenty adult participants viewed a demonstration of a robot that could “feel” tactile stimulation through a haptic sensor on its hand and “see” changes in light through a photoreceptor at the level of the eyes; the robot responded to touch or changes in light by moving a contralateral digit. During EEG collection, participants engaged in a joint task that involved sending tactile stimulation to a partner (robot or human) and receiving tactile stimulation back. Tactile stimulation sent by the participant was initiated by a button press and was delivered 1500 ms later via an inflatable membrane on the hand of the human or on the haptic sensor of the robot partner. Stimulation to the participant’s finger (from the partner) was sent on a fixed schedule, regardless of partner type. We analyzed activity of the sensorimotor mu rhythm during anticipation of tactile stimulation to the right hand, comparing mu activity at central electrode sites when participants believed that tactile stimulation was initiated by a robot or a human, and to trials in which “nobody” received stimulation. There was a significant difference in contralateral mu rhythm activity between anticipating stimulation from a human partner and the “nobody” condition. This effect was less pronounced for anticipation of stimulation from the robot partner. Analyses also examined beta rhythm responses to the execution of the button press, comparing oscillatory activity when participants sent tactile stimulation to the robot or the human partner. The extent of beta rebound at frontocentral electrode sites following the button press differed between conditions, with a significantly larger increase in beta power when participants sent tactile stimulation to a robot partner compared to the human partner. This increase in beta power may reflect greater predictably in event outcomes. This new paradigm and the novel findings advance the neuroscientific study of human–robot interaction.

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The Role of Functional Affordances in Socializing Robots

Cited by 28 publications

References 32 publications

Deliberative and Conceptual Inference in Service Robots

Deliberative and Conceptual Inference in Service Robots

A Survey of Behavioral Models for Social Robots

Sensorimotor Oscillations During a Reciprocal Touch Paradigm With a Human or Robot Partner

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