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About

Yunrong Guo is a prominent robotics researcher specializing in physics-based simulation, reinforcement learning, and robot skill acquisition. His work sits at the critical intersection of high-performance computing and robot learning, focusing on building the simulation infrastructure that enables modern robotic systems to develop complex behaviors efficiently. Guo's most impactful contribution is his involvement in **Isaac Gym** (2021, 322 citations), NVIDIA's groundbreaking GPU-accelerated physics simulation platform that revolutionized robot learning by enabling end-to-end training directly on the GPU, dramatically accelerating policy learning for a wide range of robotic tasks. Building on this foundation, he co-developed **Orbit** (2023, 226 citations), a unified and modular simulation framework powered by NVIDIA Isaac Sim, which further democratized robot learning research through photorealistic environments and high-fidelity rigid and deformable body simulation. His work on **Factory** (2022, 54 citations) tackled the notoriously difficult problem of robotic assembly by enabling fast, accurate contact simulation, helping bring simulation-driven research to one of robotics' oldest challenges. With over 600 citations across his key publications, Guo's contributions have become essential infrastructure for the global robot learning research community.