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Tag: KI for Robotics
  • Research Highlight

    Advancements in Elastic Deformable-Object Manipulation

    Prof. Daehyung Park’s research group developed implicit neural-representation learning for deformable object manipulation in real-world....read more

    Assembly Grasping & Manipulation KI for Robotics Logistics and Manufacturing Robot Learning: Reinforcement Learning Robotics
  • Research Highlight

    Dual-Arm Teleoperation for Automating Unstructured Bimanual Tasks in Manufacturing

    Professor Ryu’s group has developed a dual-arm teleoperation system to automate complex, contact-rich bimanual tasks through remote and virtual human demonstrations....read more

    Haptics KI for Robotics Skill Transfer Teleoperation Telerobotics
  • Research Highlight Top Story

    Learned-Imitation for Navigation in Cluttered Space

    Prof. Han-Lim Choi’s research team was the 1st winner at the BARN Challenge at ICRA 2024 (Yokohama, Japan) with an imitation-learning based algorithm....read more

    KI for Robotics
  • Research Highlight

    Constrained Disturbance Observer

    In robotics, achieving accurate motion tracking is a basic and fundamental challenge. For accurate tracking, robust control methods such as the Disturbance Observer (DOB) have been studied to eliminate uncertainties such as external forces and joint friction. However, the need for safe interactions has grown as robots increasingly share workspaces with humans. This creates a paradox: robots must not only track accurately but also respond safely to external forces that occur by human interactions. To address this, Prof. Min Jun Kim’s group at KAIST developed the Constrained Disturbance Observer (CDOB) framework, which adds intelligence to the ordinary DOB through optimization techniques. Consequently, while maintaining accuracy during free motion, robots are also capable of managing safety constraints and interacting with unknown environments....read more

    KI for Robotics
  • Research Highlight

    Two-Year Journey of Team KAIST to the Finals of MBZIRC 2023

    Team KAIST, comprising students from the labs MORIN (of Prof. Jinwhan Kim) and USRG (of Prof. Hyunchul Shim), in partnership with PABLO AIR, won the 2nd Place Winner position in the MBZIRC Maritime Grand Challenge, one of the world’s largest robotics competition....read more

    Fundamental Research on Future Robotics Guidance KI for Robotics Marine robotics Navigation and Control Vehicle Dynamics
  • Research Highlight

    Ultra-strong soft gripper that lifts 100kg with 130g

    Prof. Lee’s group, in collaboration with KIST, innovated a lightweight, cost-effective soft robotic gripper capable of securely holding 100kg objects, promising enhanced utility in various domestic and industrial applications....read more

    Cooperative Robot Fundamental Research on Future Robotics KI for Robotics Manipulation Soft Gripper
  • Research Highlight

    2.5D Laser-Cutting-Based Customized Fabrication of Long-Term Wearable Textile sEMG Sensor

    Prof. Jung Kim’s group has developed a 2.5D laser cutting method to accelerate customized sEMG sensor fabrication from design to production. sEMG sensors measure human muscle activity and are widely used in wearable systems for human-machine interaction. In order to use sEMG sensors for a long time in daily life, it is necessary to develop a sensor that can be customized and worn easily and does not affect the signal due to movement. This customizable textile-based sEMG sensor provides high wearing comfort and improves the sensor signal quality through stable contact....read more

    Fundamental Research on Future Robotics intention recognition KI for Robotics sEMG sensor wearable robotics
  • Introduction to KI

    KI for Robotics

    The Urban Robotics Lab (URL) focuses on research and development of robotics technologies for smart cities....read more

    KI for Robotics
  • Research Highlight

    Deep Generative Diffusion Models

    AAILab, KAIST has developed various deep generative model structures and inference methods to synthesize realistic images through diffusion processes. ...read more

    Deep Generative Models Diffusion Models KI for Robotics Machine Learning
  • Research Highlight

    Self-Supervised Learning to Distill Hierarchy in High-Dimensional Dynamic Systems

    Prof. Han-Lim Choi’s research team has developed a learning framework to control a high-dimensional robotic system that distills underlying hierarchical structure in robot motion data. By learning high-level intentions as well as low-level control actions, the proposed framework enables adaptation of policy learned from a certain task to much more diverse sets of tasks. ...read more

    AI for Cooperative Robots Deep Learning High-Dimensional Systems Interpretable AI KI for Robotics Reinforcement Learning Representation Learning Robust Intelligence Under Uncertainty

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