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About

Tutian Tang is a rising researcher at the forefront of robotic manipulation, tactile perception, and embodied AI. Their work focuses on bridging the gap between physical interaction and digital perception, with major contributions in capturing forceful hand-object interactions and developing multiphysics simulation platforms. Tang’s most cited paper (24 citations) introduces a deep learning-powered stretchable tactile array that captures deformable object interactions for applications in virtual reality, telemedicine, and robotics. Their benchmark H2O (23 citations) systematically explores visual human-human object handover, a critical yet underexplored area for robotics and cognitive science. Tang also developed RFUniverse (16 citations), a multiphysics simulation platform enabling embodied AI agents to learn complex real-world phenomena. Notable achievements include DiPGrasp, a differentiable grasp planner compatible with various robot grippers, and RFTrans, which leverages refractive flow for transparent object manipulation—a notoriously challenging problem. With additional work on tactile-based needle-threading and language-guided dexterous grasping, Tang’s research demonstrates a consistent focus on enabling robots to perceive and interact with complex, real-world objects through multimodal sensing and intelligent planning.