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Publication Open Access A computational multicriteria optimization approach to controller design for pysical human-robot interaction(Institute of Electrical and Electronics Engineers (IEEE), 2020) Tokatlı, Ozan; Patoğlu, Volkan; Department of Mechanical Engineering; Aydın, Yusuf; Başdoğan, Çağatay; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 125489Physical human-robot interaction (pHRI) integrates the benefits of human operator and a collaborative robot in tasks involving physical interaction, with the aim of increasing the task performance. However, the design of interaction controllers that achieve safe and transparent operations is challenging, mainly due to the contradicting nature of these objectives. Knowing that attaining perfect transparency is practically unachievable, controllers that allow better compromise between these objectives are desirable. In this article, we propose a multicriteria optimization framework, which jointly optimizes the stability robustness and transparency of a closed-loop pHRI system for a given interaction controller. In particular, we propose a Pareto optimization framework that allows the designer to make informed decisions by thoroughly studying the tradeoff between stability robustness and transparency. The proposed framework involves a search over the discretized controller parameter space to compute the Pareto front curve and a selection of controller parameters that yield maximum attainable transparency and stability robustness by studying this tradeoff curve. The proposed framework not only leads to the design of an optimal controller, but also enables a fair comparison among different interaction controllers. In order to demonstrate the practical use of the proposed approach, integer and fractional order admittance controllers are studied as a case study and compared both analytically and experimentally. The experimental results validate the proposed design framework and show that the achievable transparency under fractional order admittance controller is higher than that of integer order one, when both controllers are designed to ensure the same level of stability robustness.Publication Open Access A gated fusion network for dynamic saliency prediction(Institute of Electrical and Electronics Engineers (IEEE), 2022) Kocak, Aysun; Erdem, Erkut; Department of Computer Engineering; Erdem, Aykut; Faculty Member; Department of Computer Engineering; College of Engineering; 20331Predicting saliency in videos is a challenging problem due to complex modeling of interactions between spatial and temporal information, especially when ever-changing, dynamic nature of videos is considered. Recently, researchers have proposed large-scale data sets and models that take advantage of deep learning as a way to understand what is important for video saliency. These approaches, however, learn to combine spatial and temporal features in a static manner and do not adapt themselves much to the changes in the video content. In this article, we introduce the gated fusion network for dynamic saliency (GFSalNet), the first deep saliency model capable of making predictions in a dynamic way via the gated fusion mechanism. Moreover, our model also exploits spatial and channelwise attention within a multiscale architecture that further allows for highly accurate predictions. We evaluate the proposed approach on a number of data sets, and our experimental analysis demonstrates that it outperforms or is highly competitive with the state of the art. Importantly, we show that it has a good generalization ability, and moreover, exploits temporal information more effectively via its adaptive fusion scheme.
Publication Metadata only A monolithic opto-coupler based sensor for contact force detection in artificial hand(Institute of Electrical and Electronics Engineers (IEEE), 2016) N/A; Department of Mechanical Engineering; Shams, Sarmad; Lazoğlu, İsmail; PhD Student; Faculty Member; Department of Mechanical Engineering; Manufacturing and Automation Research Center (MARC); Graduate School of Sciences and Engineering; College of Engineering; N/A; 179391This paper presents a monolithic opto-coupler based force sensor design to detect the contact forces of the fingertip of the artificial hand during grasp process. Effective and precise measurement of the contact force is always a challenge for the humid and temperature varying environment. In this paper, we propose a novel design of force sensor with optical technique. The optical technique is preferred over other techniques because of its simpler electronics and less immunity to temperature variation under humid environment. Simulation results conducted using Finite Element Method (FEM) analysis confirmed the deflection is linear for the forces from 0 to ±100 N. The maximum stress found at 100 N is 252.39 MPa. Also, modal analysis is performed to ensure the sensor is durable and operative while handling different vibrating objects. Calibration experiment of the sensor is performed using multipoint calibration process and curve fitting technique.Publication Metadata only A new control architecture for physical human-robot interaction based on haptic communication(Ieee, 2014) N/A; N/A; Department of Mechanical Engineering; Aydın, Yusuf; Arghavani, Nasser; Başdoğan, Çağatay; PhD Student; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; 328776; N/A; 125489In the near future, humans and robots are expected to perform collaborative tasks involving physical interaction in various different environments such as homes, hospitals, and factories. One important research topic in physical Human-Robot Interaction (pHRI) is to develop tacit and natural haptic communication between the partners. Although there are already several studies in the area of Human-Robot Interaction, the number of studies investigating the physical interaction between the partners and in particular the haptic communication are limited and the interaction in such systems is still artificial when compared to natural human-human collaboration. Although the tasks involving physical interaction such as the table transportation can be planned and executed naturally and intuitively by two humans, there are unfortunately no robots in the market that can collaborate and perform the same tasks with us. In this study, we propose a new controller for the robotic partner that is designed to a) detect the intentions of the human partner through haptic channel using a fuzzy controller b) adjust its contribution to the task via a variable impedance controller and c) resolve the conflicts during the task execution by controlling the internal forces. The results of the simulations performed in Simulink/Matlab show that the proposed controller is superior to the stand-alone standard/variable impedance controllers.Publication Metadata only A realistic simulation environment for mri-based robust control of untethered magnetic robots with intra-operational imaging(IEEE-Inst Electrical Electronics Engineers Inc, 2020) Tiryaki, Mehmet Efe; Erin, Önder; N/A; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104Dual-use of magnetic resonance imaging (MRI) devices for robot tracking and actuation has transformed them into potential medical robotics platforms for targeted therapies and minimally invasive surgeries. In this letter, we present the dynamic simulations of anMRI-based tracking and actuation scheme, which performs intra-operational imaging while controlling untethered magnetic robots. In our realistic rigid-body simulation, we show that the robot could be controlled with a 1D projection-based position feedback while performing intra-operational echo-planar imaging (EPI). From the simulations, we observe that the velocity estimation error is the main source of the controller instability for low MRI sequence frequencies. To minimize the velocity estimation errors, we constrain the controller gains according to maximum closed-loop rates achievable for different sequence durations. Using the constrained controller in simulations, we confirm that EPI imaging could be introduced to the sequence as an intra-operational imaging method. Although the intro-operational imaging increases the position estimation error to 2.0 mm for a simulated MRI-based position sensing with a 0.6 mm Gaussian noise, it does not cause controller instability up to 128 k-space lines.With the presented approach, continuous physiological images could be acquired during medical operations while a magnetic robot is actuated and tracked inside an MRI device.Publication Metadata only An adaptive admittance controller for collaborative drilling with a robot based on subtask classification via deep learning(Elsevier, 2022) Aydin, Yusuf; N/A; N/A; N/A; Department of Mechanical Engineering; Güler, Berk; Niaz, Pouya Pourakbarian; Madani, Alireza; Başdoğan, Çağatay; Master Student; Master Student; Master Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; 125489In this paper, we propose a supervised learning approach based on an Artificial Neural Network (ANN) model for real-time classification of subtasks in a physical human-robot interaction (pHRI) task involving contact with a stiff environment. In this regard, we consider three subtasks for a given pHRI task: Idle, Driving, and Contact. Based on this classification, the parameters of an admittance controller that regulates the interaction between human and robot are adjusted adaptively in real time to make the robot more transparent to the operator (i.e. less resistant) during the Driving phase and more stable during the Contact phase. The Idle phase is primarily used to detect the initiation of task. Experimental results have shown that the ANN model can learn to detect the subtasks under different admittance controller conditions with an accuracy of 98% for 12 participants. Finally, we show that the admittance adaptation based on the proposed subtask classifier leads to 20% lower human effort (i.e. higher transparency) in the Driving phase and 25% lower oscillation amplitude (i.e. higher stability) during drilling in the Contact phase compared to an admittance controller with fixed parameters.Publication Metadata only Are tutor robots for everyone? the influence of attitudes, anxiety, and personality on robot-led language learning(Springer, 2022) Kumkale, G. Tarcan; Department of Psychology; Department of Psychology; Department of Psychology; N/A; Department of Psychology; Küntay, Aylin C.; Kanero, Junko; Oranç, Cansu; Koşkulu, Sümeyye; Göksun, Tilbe; Faculty Member; Researcher; Researcher; Master Student; Faculty Member; Department of Psychology; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; 178879; N/A; N/A; N/A; 47278Do some individuals benefit more from social robots than others? Using a second language (L2) vocabulary lesson as an example, this study examined how individual differences in attitudes toward robots, anxiety in learning L2, and personality traits may be related to the learning outcomes. One hundred and two native Turkish-speaking adults were taught eight English words in a one-on-one lesson either with the NAO robot (N = 51) or with a human tutor (N = 51). The results in both production and receptive language tests indicated that, following the same protocol, the two tutors are fairly comparable in teaching L2 vocabulary. Negative attitudes toward robots and anxiety in L2 learning impeded participants from learning vocabulary in the robot tutor condition whereas the personality trait of extroversion negatively predicted vocabulary learning in the human tutor condition. This study is among the first to demonstrate how individual differences can affect learning outcomes in robot-led sessions and how general attitudes toward a type of device may affect the ways humans learn using the device.Publication Metadata only BirdBot achieves energy-efficient gait with minimal control using avian-inspired leg clutching(American Association for the Advancement of Science (AAAS), 2022) Badri-Sprowitz, Alexander; Sarvestani, Alborz Aghamaleki; Daley, Monica A.; N/A; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104Designers of legged robots are challenged with creating mechanisms that allow energy-efficient locomotion with robust and minimalistic control. Sources of high energy costs in legged robots include the rapid loading and high forces required to support the robot's mass during stance and the rapid cycling of the leg's state between stance and swing phases. Here, we demonstrate an avian-inspired robot leg design, BirdBot, that challenges the reliance on rapid feedback control for joint coordination and replaces active control with intrinsic, mechanical coupling, reminiscent of a self-engaging and disengaging clutch. A spring tendon network rapidly switches the leg's slack segments into a loadable state at touchdown, distributes load among joints, enables rapid disengagement at toe-off through elastically stored energy, and coordinates swing leg flexion. A bistable joint mediates the spring tendon network's disengagement at the end of stance, powered by stance phase leg angle progression. We show reduced knee-flexing torque to a 10th of what is required for a nonclutching, parallel-elastic leg design with the same kinematics, whereas spring-based compliance extends the leg in stance phase. These mechanisms enable bipedal locomotion with four robot actuators under feedforward control, with high energy efficiency. The robot offers a physical model demonstration of an avian-inspired, multiarticular elastic coupling mechanism that can achieve self-stable, robust, and economic legged locomotion with simple control and no sensory feedback. The proposed design is scalable, allowing the design of large legged robots. BirdBot demonstrates a mechanism for self-engaging and disengaging parallel elastic legs that are contact-triggered by the foot's own lever-arm action.Publication Open Access Children's reliance on the non-verbal cues of a robot versus a human(Public Library of Science, 2019) Verhagen J.; Van Den Berghe R.; Oudgenoeg-Paz O.; Leseman P.; Department of Psychology; Küntay, Aylin C.; Faculty Member; Department of Psychology; College of Social Sciences and Humanities; 178879Robots are used for language tutoring increasingly often, and commonly programmed to display non-verbal communicative cues such as eye gaze and pointing during robot-child interactions. With a human speaker, children rely more strongly on non-verbal cues (pointing) than on verbal cues (labeling) if these cues are in conflict. However, we do not know how children weigh the non-verbal cues of a robot. Here, we assessed whether four- to six-year-old children (i) differed in their weighing of non-verbal cues (pointing, eye gaze) and verbal cues provided by a robot versus a human; (ii) weighed non-verbal cues differently depending on whether these contrasted with a novel or familiar label; and (iii) relied differently on a robot's non-verbal cues depending on the degree to which they attributed human-like properties to the robot. The results showed that children generally followed pointing over labeling, in line with earlier research. Children did not rely more strongly on the non-verbal cues of a robot versus those of a human. Regarding pointing, children who perceived the robot as more human-like relied on pointing more strongly when it contrasted with a novel label versus a familiar label, but children who perceived the robot as less human-like did not show this difference. Regarding eye gaze, children relied more strongly on the gaze cue when it contrasted with a novel versus a familiar label, and no effect of anthropomorphism was found. Taken together, these results show no difference in the degree to which children rely on non-verbal cues of a robot versus those of a human and provide preliminary evidence that differences in anthropomorphism may interact with children's reliance on a robot's non-verbal behaviors.Publication Metadata only Chiropractic alters TMS induced motor neuronal excitability: preliminary findings(Springer International Publishing Ag, 2014) Haavik, Heidi; Niazi, Imran Khan; Duehr, Jens; Kinget, Mat; Ugincius, Paulius; Department of Physics; N/A; Sebik, Oğuz; Yılmaz, Gizem; Türker, Kemal Sıtkı; Researcher; PhD Student; Faculty Member; Department of Physics; College of Sciences; Graduate School of Health Sciences; School of Medicine; Koç University Hospital; N/A; N/A; 6741The objective of this study was to use the electromyography (EMG) via surface and intramuscular single motor unit recordings to further characterize the immediate sensorimotor effects of spinal manipulation and a control intervention using TMS. The results provide evidence that spinal manipulation of dysfunctional spinal segments increases low threshold motoneurone excitability.