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Multi-Grasp Classification for the Control of Robot Hands Employing Transformers and Lightmyography Signals.

, , , , , and . EMBC, page 1-6. IEEE, (2023)

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Electromyography Based Gesture Decoding Employing Few-Shot Learning, Transfer Learning, and Training From Scratch., , , , and . IEEE Access, (2023)On Human Grasping and Manipulation in Kitchens: Automated Annotation, Insights, and Metrics for Effective Data Collection., , , , , , and . ICRA, page 11329-11335. IEEE, (2023)Employing Multi-Layer, Sensorised Kirigami Grippers for Single-Grasp Based Identification of Objects and Force Exertion Estimation., , , , , , , , and . IROS, page 6433-6440. (2023)Why, in Deep Learning, Non-smooth Activation Function Works Better Than Smooth Ones., , and . Decision Making Under Uncertainty and Constraints, 217, Springer, (2023)On Semi-Autonomous Robotic Telemanipulation Employing Electromyography Based Motion Decoding and Potential Fields., , , , and . IROS, page 6991-6997. (2023)Electromyography-Based, Robust Hand Motion Classification Employing Temporal Multi-Channel Vision Transformers., , , , , , , and . BioRob, page 1-8. IEEE, (2022)Deep Reinforcement Learning Control of an Autonomous Wheeled Robot in a Challenge Task: Combined Visual and Dynamics Sensoring., , , , , and . ICAR, page 368-373. IEEE, (2019)Comparing Human and Robot Performance in the Execution of Kitchen Tasks: Evaluating Grasping and Dexterous Manipulation Skills., , , , , , and . Humanoids, page 518-525. IEEE, (2022)Scalable. Intuitive Human to Robot Skill Transfer with Wearable Human Machine Interfaces: On Complex, Dexterous Tasks., , , , , , , and . IROS, page 6318-6325. (2023)On EMG Based Dexterous Robotic Telemanipulation: Assessing Machine Learning Techniques, Feature Extraction Methods, and Shared Control Schemes., , , , , and . IEEE Access, (2022)