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About

Yupeng Zheng is a rising researcher at the forefront of embodied AI and 3D scene understanding, with a focus on bridging language, vision, and robotic manipulation. His work centers on developing intelligent systems that can interpret open-ended human language to interact with complex 3D environments. In his highly cited 2024 paper, *GaussianGrasper*, Zheng pioneered the integration of 3D Gaussian splatting with language models to create open-vocabulary robotic grasping systems. This work, which has already garnered 37 citations, enables robots to understand and execute manipulation commands based on natural language directives, a critical step toward more intuitive human-robot collaboration. Prior to this, Zheng made significant contributions to temporal reasoning in 3D vision with his 2023 work on the *3D Implicit Transporter*. This paper, cited 15 times, introduced a novel method for discovering temporally consistent keypoints across video sequences—a breakthrough that overcomes the limitations of previous spatial-only alignment techniques. By ensuring keypoints remain stable over time, this work has important implications for long-horizon robotic tasks and dynamic scene understanding. Zheng’s research is defining new capabilities at the intersection of computer vision, natural language processing, and robotics.