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Çмú´ëȸ

Korea Robotics Society

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±¹³»Çмú´ëȸ

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Mechanical Intelligence is the ability of the physical mechanisms or robotic structures that can respond to changes in the environment, and to perform complex actions and movements without precise feedback control. Material properties and structural design of the mechanisms with mechanical intelligence may help simplify the system and improve efficiency and productivity. I will present an mechanical intelligence-based design and manufacture of robots from folded sheets of novel “smart” materials, such as the composite plastic, the metallic glass, and various soft polymers including continuum materials and fabrics. The Primary advantages of folded robot design are that the inherent accessibility and low cost of the method permits designers to get design feedback early and often via fast prototyping cycles. While prototyping is relatively fast, the complexity of multi-layer articulated designs can be time-consuming and unwieldy to design. Using the folding structure, we can mimic organisms in nature and abstract principles for robotic applications. Today's talk shows the robot design with mechanical intelligence based on kinematics and dynamics of the mechanisms that are inspired by nature. These folded robots can meet the demand for an economical robotic platform for both commercial and research applications. By combining this new methodology with Mechanical intelligence based designs, robot performance can be improved to an extraordinary degree, even better than the performance of natural organisms that have evolved unbelievable mechanical abilities.
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Distributed formation control: From rigidity theory to implementations
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Visual Sensing and Perception for Autonomous Driving
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Visual sensing and perception are fundamental building blocks for achieving safe and reliable autonomous driving. This talk will explore the critical role of visual sensors such as cameras, event camera, Lidar, and Radar, and cutting-edge computer vision techniques in enabling autonomous vehicles to understand their surroundings. I will discuss various deep learning algorithms used to extract meaningful information from visual data and also address challenges and limitations of visual perception in autonomous driving. Finally, I will explore emerging trends and future directions in this rapidly evolving field.
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Bridging Perception with Robot Manipulation
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The convergence of robotic perception and manipulation has the potential to transform how systems interact with the world, spanning fields from industrial automation to complex human-robot collaboration. Despite significant advancements, many robotic platforms still face challenges in achieving the necessary precision and adaptability due to limitations in sensory integration and real-time control. This presentation will explore advanced methodologies that bridge the gap between robotic perception and manipulation, leveraging state-of-the-art techniques in control and imaging. Key research developments will be highlighted, including the use of handheld robots for visual servoing and the implementation of hybrid force/vision control strategies that enable precise and safe manipulation. In the area of brain tumor therapy, we introduce a system that employs real-time imaging and SLAM-based light therapy to support minimally invasive procedures. Additionally, our work toward a fully automated COVID-19 sampling robot will be discussed, showcasing image-based control for multi-degree-of-freedom manipulators and force sensor-driven control to enhance safe and effective sample collection. Finally, we delve into the application of Vision Language Models (VLM) to extract semantic information from visual data, enabling intuitive, intent-driven control of multi-fingered robotic hands and broadening the scope of applications across both structured and unstructured environments. These contributions highlight the growing synergy between perception and manipulation, paving the way for more responsive and intelligent robotic systems across medical and industrial applications.
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