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About

Xinlei Pan is a researcher at the intersection of deep reinforcement learning, robot learning, and AI safety, whose work addresses some of the most pressing challenges in deploying intelligent systems in real-world environments. Pan's most influential contribution, "Risk Averse Robust Adversarial Reinforcement Learning" (2019, 64 citations), tackles the critical problem of policy overfitting and catastrophic failure avoidance by integrating risk-aware objectives with adversarial training — a significant step toward safer autonomous systems. Complementing this, his work on characterizing and exposing vulnerabilities in deep reinforcement learning models (52 citations) has helped define the landscape of adversarial threats facing DRL systems. Notably, Pan also pioneered investigations into privacy leakage in reinforcement learning, revealing how behavioral patterns can expose sensitive training data — a largely unexplored frontier at the time. Beyond security and robustness, his research extends into robot locomotion, where he developed zero-shot imitation learning frameworks for legged robot visual navigation, reducing reliance on costly expert demonstrations. With over 200 cumulative citations across his body of work, Pan has established himself as a thoughtful and impactful voice in trustworthy, efficient, and safe machine learning for robotics and autonomous systems.