Hooshang Nayyeri
Papers
2
Total Citations
13
H-Index
2
About
Hooshang Nayyeri is a rising star in robotic manipulation, whose research centers on bridging the gap between high-level human intent and low-level robot control in open-world environments. His major contributions lie in developing frameworks that enable robots to autonomously explore, understand, and interact with complex, unstructured spaces. Nayyeri’s pioneering work on the RoboEXP system introduced the novel task of interactive scene exploration, where robots autonomously generate action-conditioned scene graphs (ACSGs) that capture both geometric and semantic information—a critical step toward truly adaptive manipulation. Building on this, his highly cited 2025 paper on the Iterative Keypoint Reward (IKER) framework (8 citations) presents a real-to-sim-to-real approach that uses vision-language models to generate flexible, human-aligned reward functions, allowing robots to learn complex tasks through iterative feedback without manual programming. These contributions, though early in his career, have already garnered significant attention, demonstrating the potential to transform how robots perceive and act in the world. Nayyeri’s work is essential reading for anyone interested in the future of autonomous, human-centric robotics.
Research Focus
Key Achievements
Top Papers
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