About the PI
he/him. /’ljəŋ²-fəŋ¹-tʰɑu²’/. 陶凌峰
Dr. Lingfeng Tao is an assistant professor of Robotics and Mechatronics at Kennesaw State University, leads research at the intersection of robotics, AI, and human-machine collaboration. Dr. Tao’s research focuses on Learning-Based Robot Control, Autonomous Robots, and Human-Machine Systems, aiming to enhance robotic learning capabilities and intelligence for autonomous task execution and seamless human-robot collaboration. His work spans dexterous manipulation, multi-robot teaming, intelligent manufacturing, and human-robot interaction, with an emphasis on natural and intuitive cooperation between robots and human operators. Dr. Tao has been publishing in top-tier robotics journals and conferences such as JINT, IEEE RA-L, SMC, ICRA, IROS and ROBIO. He has secured over $800k research funding as PI and Co-PIs from National Science Foundation (NSF) and contributing to a total of over $3.8 million in successful funding proposals.
Intelligent Dexterity (InDex) Lab
The MIR Lab, within the Department of Robotics and Mechatronics Engineering at Kennesaw State University, is conducting cutting-edge intelligent robotics research in the areas of Learning-Based Robot Control, Autonomous Robots, and Human-Machine Systems, with a focus on improving the learning capability and intelligence of the robot to autonomously complete tasks or adapt and cooperate with the human operator naturally and intuitively. The applications include medical robots, multi-robot teaming, intelligent manufacturing, and human-robot collaboration.
News
[Award][09/2025] I am happy to share that our proposal, ‘Risk-Aware HRI via Multi-Modal Simulation and Physics-Informed VLA Model,’ has been awarded by the Nvidia Academic Grant Program. I’ll serve as the PI of this project.
[Paper][07/2025] Great news! our paper ‘PlasTrack: Path-Independent Plasma-Induced Attacks on mmWave Sensing Tracking‘ has been accepted to the 𝘐𝘌𝘌𝘌 𝘊𝘰𝘯𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘰𝘯 𝘊𝘰𝘮𝘮𝘶𝘯𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴 𝘢𝘯𝘥 𝘕𝘦𝘵𝘸𝘰𝘳𝘬 𝘚𝘦𝘤𝘶𝘳𝘪𝘵𝘺 (𝘊𝘕𝘚) 2025.
[Paper][06/2025] Our paper ‘Bio-Skin: A cost-effective thermostatic tactile sensor enabling multi-modal force and temperature detection‘ has been accepted by IEEE IROS 2025.
[Paper][06/2025] Our paper ‘Adaptive Anomaly Recovery for Telemanipulation: A Diffusion Model Approach to Vision-Based Tracking‘ has been accepted by IEEE IROS 2025.
[Paper][06/2025] Our paper ‘VRobotix: A Scalable and Cost-Effective Virtual-Reality-Based Robotic Manipulation Dataset Generation Framework‘ has been accepted by IEEE IROS 2025.
[Paper][06/2025] Our paper ‘DexPour: Effective and Efficient High-DoF Robotic Hand Liquid Pouring Via Hierarchical Reward with Approximated Proxy Abstraction‘ has been accepted by IEEE IROS 2025.
[Paper][03/2025] Great news! Our paper ‘mmVanish: Extending the Vanish Attack for Multi-Radar Exploitation of mmWave Sensing with Meta-material Tags‘ is published on the Sensors S&P 2025 and received the best paper award.
[Paper][01/2025] Great news! Our paper ‘Stable In-Hand Manipulation with Finger-Specific Multi-Agent Shadow Critic Consensus and Information Sharing.’ is published on the top-tier robotics journal IEEE Robotics and Automation Letters.
[Conference][10/2024] Our graduate student Haoyang Wang presented the paper ‘Real-time Dexterous Telemanipulation with an End-Effect-Oriented Learning-based Approach‘ in IEEE IROS 2024 at Abu Dhabi.
[Robot][09/2024] We just purchased a Bambu Lab P1P 3D Printer, it will be used for tactile sensors and haptic device research.
[Team][08/2024] Welcome our new graduate student researchers, Haoran Guo, and Haoyang Wang. Their research focuses on learning-based dexterous manipulation and multi-modal tactile sensing.
[Award][08/2024] I am happy to share that our proposal, ‘Dexterous In-Hand Telemanipulation with End-Effector-Based Control and Safety-Aware Perception Feedback for Human-Robot Collaborative Tasks,’ has been awarded by the National Science Foundation. I’ll serve as the PI of this project.
[Paper][07/2024] Great news! Our paper ‘Real-time Dexterous Telemanipulation with an End-Effect-Oriented Learning-based Approach.’ has been accepted by IEEE IROS 2024. It will be presented in Abu Dhabi in Oct 2024.
Sponsors
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