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Al-Saadi, Zaid Rassim Mohammed

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Zaid Rassim Mohammed

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Al-Saadi

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Al-Saadi, Zaid Rassim Mohammed

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2021 - 20232

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    Resolving conflicts during human-robot co-manipulation
    (ACM SIGAI, 2023) Aydın, Yusuf; Küçükyılmaz, Ayşe; Department of Mechanical Engineering; N/A; N/A; Başdoğan, Çağatay; Al-Saadi, Zaid Rassim Mohammed; Hamad, Yahya M; Faculty Member; PhD Student; PhD Student; Department of Mechanical Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 125489; N/A; N/A
    This paper proposes a machine learning (ML) approach to detect and resolve motion conflicts that occur between a human and a proactive robot during the execution of a physically collaborative task. We train a random forest classifier to distinguish between harmonious and conflicting human-robot interaction behaviors during object co-manipulation. Kinesthetic information generated through the teamwork is used to describe the interactive quality of collaboration. As such, we demonstrate that features derived from haptic (force/torque) data are sufficient to classify if the human and the robot harmoniously manipulate the object or they face a conflict. A conflict resolution strategy is implemented to get the robotic partner to proactively contribute to the task via online trajectory planning whenever interactive motion patterns are harmonious, and to follow the human lead when a conflict is detected. An admittance controller regulates the physical interaction between the human and the robot during the task. This enables the robot to follow the human passively when there is a conflict. An artificial potential field is used to proactively control the robot motion when partners work in harmony. An experimental study is designed to create scenarios involving harmonious and conflicting interactions during collaborative manipulation of an object, and to create a dataset to train and test the random forest classifier. The results of the study show that ML can successfully detect conflicts and the proposed conflict resolution mechanism reduces human force and effort significantly compared to the case of a passive robot that always follows the human partner and a proactive robot that cannot resolve conflicts.
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    PublicationOpen Access
    A novel haptic feature set for the classification of interactive motor behaviors in collaborative object transfer
    (Institute of Electrical and Electronics Engineers (IEEE), 2021) Küçükyılmaz, Ayşe; Department of Mechanical Engineering; Başdoğan, Çağatay; Şirintuna, Doğanay; Al-Saadi, Zaid Rassim Mohammed; Faculty Member; Department of Mechanical Engineering; College of Engineering; Graduate School of Sciences and Engineering; 125489; N/A; N/A
    Haptics provides a natural and intuitive channel of communication during the interaction of two humans in complex physical tasks, such as joint object transportation. However, despite the utmost importance of touch in physical interactions, the use of haptics is under-represented when developing intelligent systems. This article explores the prominence of haptic data to extract information about underlying interaction patterns within physical human-human interaction (pHHI). We work on a joint object transportation scenario involving two human partners, and show that haptic features, based on force/torque information, suffice to identify human interactive behavior patterns. We categorize the interaction into four discrete behavior classes. These classes describe whether the partners work in harmony or face conflicts while jointly transporting an object through translational or rotational movements. In an experimental study, we collect data from 12 human dyads and verify the salience of haptic features by achieving a correct classification rate over 91% using a Random Forest classifier.