Mark Yim

Mark Yim is affiliated with the University of Pennsylvania in the United States and specializes in Engineering, with a primary focus on Mechanical Engineering and Biomedical Engineering. Their research also covers Computer Vision and Pattern Recognition, Control and Systems Engineering, and Computer Networks and Communications.

The scientist's main areas of study include Modular Robots and Swarm Intelligence, Soft Robotics and Applications, Robotic Path Planning Algorithms, Micro and Nano Robotics, Advanced Materials and Mechanics, Robot Manipulation and Learning, and Robotic Locomotion and Control.

Mark Yim has published extensively in several prominent venues. Frequent publication venues include:

• arXiv (Cornell University)
• IEEE Robotics and Automation Letters
• IEEE Transactions on Robotics
• 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• Journal of Mechanisms and Robotics

Mark Yim's recent papers exemplify a focus on modular robotics and variable topology structures. Key publications are:

• SMORES-EP, a modular robot with parallel self-assembly, 2022, Autonomous Robots
• Topological Reconfiguration Planning for a Variable Topology Truss, 2021, Journal of Mechanisms and Robotics
• Coverage Control in Multi-Robot Systems via Graph Neural Networks, 2022, 2022 International Conference on Robotics and Automation (ICRA)
• Reconfiguration Solution of a Variable Topology Truss: Design and Experiment, 2020, IEEE Robotics and Automation Letters
• Polygon-Based Random Tree Search Planning for Variable Geometry Truss Robot, 2020, IEEE Robotics and Automation Letters

Collaborative work is a significant part of Mark Yim's research efforts. Frequent co-authors include:

• TaeWon Seo
• James H. Pikul
• Gedaliah Knizhnik
• Jangho Bae
• Gregory M. Campbell

The research topics and their wide scope indicate an interdisciplinary approach, combining aspects of engineering design, materials science, and autonomous systems. The work involving modular robots and swarm intelligence integrates hardware innovation with algorithmic development, notably in path planning and control. The engagement with micro and nano robotics further diversifies the scale at which this scientist operates, bridging from macroscale robot structures to microscale device applications.

Mark Yim has contributed to both experimental and computational aspects of robotics. This includes the study of variable topology truss systems, which involves reconfigurable mechanical structures, as well as methods for multi-robot coordination using advanced neural network techniques. The combination of contributions in physical robot systems and robotic learning methods reflects a hybrid research strategy spanning mechanical and software systems engineering.