Interacting with an artificial partner: modeling the role of emotional aspects

Cattinelli, Isabella; Goldwurm, Massimiliano; Borghese, N. Alberto

doi:10.1007/s00422-008-0254-9

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…In other words, the objective of the reactive module consists in building a policy which, given a context ctx , returns an action a to be executed. The execution of such actions, in particular, physically translates, in our case, in the pronunciation of personalized sentences in natural language towards a user and, similarly to [ 10 ], in the contextualized change of facial expressions. Since, however, these actions internally constitute transitions in a state-transition system [ 16 ], we propose a new form of parametric actions, inspired by those used in classical planning [ 25 ], which allow to represent the state-transition system in an implicit and compact way.…”

Section: Reactive Reasoningmentioning

confidence: 99%

A Mind-inspired Architecture for Adaptive HRI

Umbrico

Benedictis

Fracasso

et al. 2022

Int J of Soc Robotics

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One of the main challenges of social robots concerns the ability to guarantee robust, contextualized and intelligent behavior capable of supporting continuous and personalized interaction with different users over time. This implies that robot behaviors should consider the specificity of a person (e.g., personality, preferences, assistive needs), the social context as well as the dynamics of the interaction. Ideally, robots should have a “mind" to properly interact in real social environments allowing them to continuously adapt and exhibit engaging behaviors. The authors’ long-term research goal is to create an advanced mind-inspired system capable of supporting multiple assistance scenarios fostering personalization of robot’s behavior. This article introduces the idea of a dual process-inspired cognitive architecture that integrates two reasoning layers working on different time scales and making decisions over different temporal horizons. The general goal is also to support an empathetic relationship with the user through a multi-modal interaction inclusive of verbal and non-verbal expressions based on the emotional-cognitive profile of the person. The architecture is exemplified on a cognitive stimulation domain where some experiments show personalization capabilities of the approach as well as the joint work of the two layers. In particular, a feasibility assessment shows the customization of robot behaviors and the adaptation of robot interactions to the online detected state of a user. Usability sessions were performed in laboratory settings involving 10 healthy participants to assess the user interaction and the robot’s dialogue performance.

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Section: Reactive Reasoningmentioning

confidence: 99%

A Mind-inspired Architecture for Adaptive HRI

Umbrico

Benedictis

Fracasso

et al. 2022

Int J of Soc Robotics

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show abstract

“…In [135] and [136], a social interaction model was developed for the AIBO dog robot to permit a robot and user to engage in bi-directional affective communication. The AIBO detected the user's facial affect and responded with its own affective movements.…”

Section: Multi-purpose Hrimentioning

confidence: 99%

“…Qlearning was also applied to train the dog to respond in a certain way to a user's emotional state. In [136], experiments were conducted to motivate the AIBO robot to behave in a certain way. Through Q-learning, the robot adapted its behavior to the human's affective state.…”

Section: Multi-purpose Hrimentioning

confidence: 99%

A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

McColl

Hong

Hatakeyama

et al. 2015

J Intell Robot Syst

104

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In Human-Robot Interactions (HRI), robots should be socially intelligent. They should be able to respond appropriately to human affective and social cues in order to effectively engage in bi-directional communications. Social intelligence would allow a robot to relate to, understand, and interact and share information with people in real-world humancentered environments. This survey paper presents an encompassing review of existing automated affect recognition and classification systems for social robots engaged in various HRI settings. Human-affect detection from facial expressions, body language, voice, and physiological signals are investigated, as well as from a combination of the aforementioned modes. The automated systems are described by their corresponding robotic and HRI applications, the sensors they employ, and the feature detection techniques and affect classification strategies utilized. This paper also discusses pertinent future research directions for promoting the development of socially intelligent robots capable of recognizing, classifying and responding to human affective states during real-time HRI.

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“…There are litter literatures about applying of learning automation in robotics field. The proximal working contain: Pierce and Kuipers used finite state automaton to describe robot world, which is called as the discrete abstract of robot world in 1994 [4]; Dean constructed a reasoning finite automaton with random output function in 1995, which is used in environmental survey and drawing environmental map [5]; EI-Osery and Jamshidi adopted random learning automaton for helping learning of multi-robot and carried out simulation result on virtual experimental environment in 2002 [6]; Wang combined finite state automaton and Q learning to realized the behavior selection and optimal control of robot in 2004 [7]; Cattinelli used probability finite state automaton to describe emotional communication between robot and people in 2008 [8].…”

Section: Introductionmentioning

confidence: 99%

Bionic Autonomous Learning Mechanism Study Based on Automaton and Applied on Robot

Cai

Hong

Wang

2011

AMM

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Under the learning automaton frame a bionic self-learning automaton which has autonomous learning mechanism is constructed based on conditioned reflex theory. The bionic self-learning automaton is a 5-tupe and mainly contains: 1) inner set, mainly are perception state set and operant set; 2) oriented core, guide the direction of autonomous learning; 3) learning core, the primitives of learning unit; 4) autonomous learning unit, implement learning computation; 5) operant entropy, measuring the self-organized degree of bionic self-learning automaton. Theory analysis is made for the designed bionic self-learning automaton in the thesis, which theoretically proves the convergence of autonomous learning mechanism in bionic self-learning automaton, namely the operant entropy will converge to minimum with the learning process. And then the designed bionic self-learning automaton which used as the mathematical abstraction and formal tool of robot and used to describe the self-learning behavior of robot is applied to motion control of two-wheeled self-balanced robot. The state sets of bionic self-learning automaton is described by tilt angle and tilt angle rate of robot, and the operant sets is described by control signal of left motor and right motor. Robot doesn’t have t motion balanced skill in initial state, and with the learning proceeding the operant entropy will gradually decrease. After about twenty-five rounds training, the operant entropy gradually tends to minimum. So robot gradually learned the motion balanced skill.

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Interacting with an artificial partner: modeling the role of emotional aspects

Cited by 6 publications

References 20 publications

A Mind-inspired Architecture for Adaptive HRI

A Mind-inspired Architecture for Adaptive HRI

A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

Bionic Autonomous Learning Mechanism Study Based on Automaton and Applied on Robot

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