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About

Yijie Guo is an emerging robotics researcher whose work spans robot manipulation, human-robot collaboration, and humanoid locomotion — areas that sit at the frontier of making robots genuinely useful in complex, real-world environments. Their most recognized contributions include foundational work on the Robotic View Transformer (RVT) series, which addresses a critical challenge in 3D object manipulation: achieving precise, scalable robot control without the prohibitive computational cost of explicit voxel-based representations. The follow-up RVT-2 demonstrated remarkable millimeter-level precision from minimal demonstrations, earning 33 citations shortly after publication. Equally impactful is Guo's work on whole-body humanoid locomotion using reinforcement learning with human motion references, also garnering 33 citations and advancing how humanoid robots learn natural, complex movement. In parallel, Guo has contributed meaningfully to human-robot collaboration in construction settings, developing systems for physical object handover and grip-state recognition that prioritize safety in close-contact workflows. With multiple high-impact papers across manipulation, locomotion, and collaborative robotics — collectively accumulating over 100 citations — Yijie Guo represents a versatile and rapidly rising voice in embodied AI and robot learning research.