Search Results - Yacoub, K*
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Robotic learning of force-based industrial manipulation tasks
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The reaction of recoil tritium atoms with hydrogen and its isotopes
Published 1981Get full text
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Human-Human co-manipulation data
Published 2020“…There are four co-manipulation trials, each one contains the following columns: 'rosbagTimestamp': Timestamp '# of samples'Force/torque signal: 'Fx', 'Fy', 'Fz', 'Tx', 'Ty', 'Tz' Object position: 'x.v', 'y.v', 'z.v', 'Rx.v', 'Ry.v', 'Rz.v', 'w.v' Follower right arm/forearm EMG: 'emg_RFsgl', 'emg_RAsgl', Object displacement (Cartisaian): 'disp_x', 'disp_y', 'disp_z'Time difference between two consequent timestamps 'step_size' The data was collected as follows: Two humans were asked to co-manipulate a load of 10 Kg (together). One human was acting like a leader, and the other one was asked to follow. …”
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Symbolic-based recognition of contact states for learning assembly skills
Published 2019Get full text
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Towards an automated masking process: a model-based approach
Published 2018Get full text
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Towards human-chatbot interaction: a virtual assistant for the ramp-up process
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Understanding human decision-making during production ramp-up using natural language processing
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Improving human robot collaboration through force/torque based learning for object manipulation
Published 2021Get full text
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Towards Industrial Robots as a Service (IRaaS): Flexibility, usability, safety and business models
Published 2022Get full text
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Compensate undesired force and torque measurements using parametric regression methods [Abstract]
Published 2016Get full text
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Towards a decision-support framework for reducing ramp-up effort in plug-and-produce systems
Published 2019Get full text
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Learning industrial robot force/torque compensation: A comparison of support vector and random forests regression
Published 2016Get full text
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