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About

Yutong Li is a rising researcher at the intersection of robotics, embodied AI, and tactile sensing, whose work focuses on enabling machines to perceive and interact with the physical world with human-like dexterity. Their major contributions span three interconnected areas: high-fidelity hand-object state reconstruction, multiphysics simulation for embodied agents, and efficient grasp planning. Li’s most cited work (2024, 24 citations) introduces a deep learning-powered stretchable tactile array that captures forceful interactions with deformable objects—a breakthrough for applications in virtual reality, telemedicine, and robotics, overcoming the challenge of occluded object deformations. They also developed RFUniverse (2023, 16 citations), a multiphysics simulation platform that models real-world coupling effects, enabling intelligent agents to learn complex household tasks. Li’s DiPGrasp (2024, 7 citations) provides a fast, differentiable grasp planner compatible with various robot grippers, advancing robotic manipulation. Their latest work, FSGlove (2025), achieves high-degree-of-freedom hand motion capture with shape-aware calibration, pushing the boundaries of biomechanics and VR. With a growing citation record and a focus on bridging simulation and reality, Li is shaping the future of physically intelligent systems.