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About

Tsung-Yen Yang is a robotics and machine learning researcher whose work sits at the intersection of embodied AI, safe reinforcement learning, and robotic manipulation. His research addresses some of the most challenging problems in deploying intelligent robots in real-world environments, spanning legged locomotion, mobile manipulation, and human-robot collaboration. Among his most impactful contributions is Adaptive Skill Coordination (ASC), which enables robots to accomplish complex long-horizon tasks such as mobile pick-and-place through coordinated visuomotor skill libraries — a paper that has garnered 36 citations since 2023. His work on safe reinforcement learning for legged locomotion (35 citations) tackles the critical challenge of keeping quadruped robots operating safely during training and deployment, a persistent bottleneck in real-world RL applications. Yang has also contributed to large-scale simulation infrastructure through Habitat 3.0 and HomeRobot, platforms enabling collaborative human-robot research in home environments. Notably, his earlier work on natural language interfaces for safe RL and spatial grounding demonstrates a consistent thread of making autonomous systems more interpretable and accessible. Across his relatively concise but high-impact publication record, Yang has established himself as a promising contributor to the next generation of capable, safe, and deployable robotic systems.