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About

Yaxin Peng is an emerging researcher at the forefront of embodied AI and robotic manipulation, specializing in Vision-Language-Action (VLA) models, language-conditioned robot control, and efficient visuomotor learning. Their most influential contribution, *TinyVLA* (2025), has garnered 49 citations and addresses a critical bottleneck in robotics: making VLA models faster and more data-efficient without sacrificing performance — a significant step toward practical deployment of intelligent robots. Peng's work spans both architectural innovation and cognitive inspiration; their *Fast and Slow Thinking* framework for robotic manipulation draws on dual-process theory from cognitive science, demonstrating a cross-disciplinary depth that enriches the field. Their *Object-Centric Instruction Augmentation* research further advances robots' ability to simultaneously understand object identity and spatial positioning — capabilities essential for real-world task execution. More recently, *ChatVLA* and a billion-parameter *Scaling Diffusion Policy* reflect Peng's ambitions in unifying multimodal understanding with robot control at scale. Collectively accumulating over 80 citations within a remarkably short publication window, Peng's research trajectory signals a researcher rapidly shaping how intelligent systems perceive, reason, and act in the physical world.