An Antisocial Social Robot: Using Negative Affect to Reinforce Cooperation in Human-Robot Interactions

Goo, Hideki Garcia; Perez, Jaime Alvarez; Contreras, Virginia

doi:10.1109/hri.2019.8673264

Cited by 2 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, recognizing actions that convey emotions is important for facilitating nonverbal communication. Furthermore, nonverbal cues with emotions may facilitate human – robot interaction (Goo et al , 2019). As a consequence, increasing focuses have been put in dyadic factors (Morency et al , 2010).…”

Section: Introductionmentioning

confidence: 99%

Affective actions recognition in dyadic interactions based on generative and discriminative models

Yang

Wang

Zhao

et al. 2020

View full text Add to dashboard Cite

Purpose Dyadic interactions are significant for human life. Most body sensor networks-based research studies focus on daily actions, but few works have been done to recognize affective actions during interactions. The purpose of this paper is to analyze and recognize affective actions collected from dyadic interactions. Design/methodology/approach A framework that combines hidden Markov models (HMMs) and k-nearest neighbor (kNN) using Fisher kernel learning is presented in this paper. Furthermore, different features are considered according to the interaction situations (positive situation and negative situation). Findings Three experiments are conducted in this paper. Experimental results demonstrate that the proposed Fisher kernel learning-based framework outperforms methods using Fisher kernel-based approach, using only HMMs and kNN. Practical implications The research may help to facilitate nonverbal communication. Moreover, it is important to equip social robots and animated agents with affective communication abilities. Originality/value The presented framework may gain strengths from both generative and discriminative models. Further, different features are considered based on the interaction situations.

show abstract

Section: Introductionmentioning

confidence: 99%