Researcher:
Hussain, Nusrah

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PhD Student

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Nusrah

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Hussain

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Hussain, Nusrah

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2019 - 201922020 - 20232

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Researcher Of Publications: search.filters.isAuthorOfPublication.2eb63313-e6ff-42d1-8437-acfeed41cc74

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    The eHRI database: a multimodal database of engagement in human-robot interactions
    (Springer, 2023) N/A; N/A; N/A; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Kesim, Ege; Numanoğlu, TuğƧe; Bayramoğlu, ƖykĆ¼ Zeynep; TĆ¼rker, Bekir Berker; Hussain, Nusrah; Sezgin, Tevfik Metin; Yemez, YĆ¼cel; Erzin, Engin; Master Student; Master Student; Master Student; Researcher; PhD Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; KoƧ Ɯniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ KoƧ University İş Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; N/A; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; N/A; N/A; 18632; 107907; 34503
    We present the engagement in human-robot interaction (eHRI) database containing natural interactions between two human participants and a robot under a story-shaping game scenario. The audio-visual recordings provided with the database are fully annotated at a 5-intensity scale for head nods and smiles, as well as with speech transcription and continuous engagement values. In addition, we present baseline results for the smile and head nod detection along with a real-time multimodal engagement monitoring system. We believe that the eHRI database will serve as a novel asset for research in affective human-robot interaction by providing raw data, annotations, and baseline results.
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    Batch recurrent Q-Learning for backchannel generation towards engaging agents
    (Institute of Electrical and Electronics Engineers (IEEE), 2019) N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Hussain, Nusrah; Erzin, Engin; Sezgin, Tevfik Metin; Yemez, YĆ¼cel; PhD Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; 34503; 18632; 107907
    The ability to generate appropriate verbal and nonverbal backchannels by an agent during human-robot interaction greatly enhances the interaction experience. Backchannels are particularly important in applications like tutoring and counseling, which require constant attention and engagement of the user. We present here a method for training a robot for backchannel generation during a human-robot interaction within the reinforcement learning (RL) framework, with the goal of maintaining high engagement level. Since online learning by interaction with a human is highly time-consuming and impractical, we take advantage of the recorded human-to-human dataset and approach our problem as a batch reinforcement learning problem. The dataset is utilized as a batch data acquired by some behavior policy. We perform experiments with laughs as a backchannel and train an agent with value-based techniques. In particular, we demonstrate the effectiveness of recurrent layers in the approximate value function for this problem, that boosts the performance in partially observable environments. With off-policy policy evaluation, it is shown that the RL agents are expected to produce more engagement than an agent trained from imitation learning.
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    PublicationOpen Access
    Speech driven backchannel generation using deep Q-network for enhancing engagement in human-robot interaction
    (International Speech Communication Association ( ISCA), 2019) Department of Computer Engineering; Hussain, Nusrah; Erzin, Engin; Sezgin, Tevfik Metin; Yemez, YĆ¼cel; PhD Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 34503; 18632; 107907
    We present a novel method for training a social robot to generate backchannels during human-robot interaction. We address the problem within an off-policy reinforcement learning framework, and show how a robot may learn to produce non-verbal backchannels like laughs, when trained to maximize the engagement and attention of the user. A major contribution of this work is the formulation of the problem as a Markov decision process (MDP) with states defined by the speech activity of the user and rewards generated by quantified engagement levels. The problem that we address falls into the class of applications where unlimited interaction with the environment is not possible (our environment being a human) because it may be time-consuming, costly, impracticable or even dangerous in case a bad policy is executed. Therefore, we introduce deep Q-network (DQN) in a batch reinforcement learning framework, where an optimal policy is learned from a batch data collected using a more controlled policy. We suggest the use of human-to-human dyadic interaction datasets as a batch of trajectories to train an agent for engaging interactions. Our experiments demonstrate the potential of our method to train a robot for engaging behaviors in an offline manner.
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    PublicationOpen Access
    Training socially engaging robots: modeling backchannel behaviors with batch reinforcement learning
    (Institute of Electrical and Electronics Engineers (IEEE), 2022) Department of Computer Engineering; Department of Electrical and Electronics Engineering; Hussain, Nusrah; Erzin, Engin; Sezgin, Tevfik Metin; Yemez, YĆ¼cel; PhD Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; 34503; 18632; 107907
    A key aspect of social human-robot interaction is natural non-verbal communication. In this work, we train an agent with batch reinforcement learning to generate nods and smiles as backchannels in order to increase the naturalness of the interaction and to engage humans. We introduce the Sequential Random Deep Q-Network (SRDQN) method to learn a policy for backchannel generation, that explicitly maximizes user engagement. The proposed SRDQN method outperforms the existing vanilla Q-learning methods when evaluated using off-policy policy evaluation techniques. Furthermore, to verify the effectiveness of SRDQN, a human-robot experiment has been designed and conducted with an expressive 3d robot head. The experiment is based on a story-shaping game designed to create an interactive social activity with the robot. The engagement of the participants during the interaction is computed from user's social signals like backchannels, mutual gaze and adjacency pair. The subjective feedback from participants and the engagement values strongly indicate that our framework is a step forward towards the autonomous learning of a socially acceptable backchanneling behavior.