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I am a first-year Ph.D. student in Computer Science at Stanford University, advised by Prof. Chelsea Finn and Prof. Dorsa Sadigh. Previously, I obtained my bachelor's degree from the Institute for Interdisciplinary Information Sciences (Yao Class) at Tsinghua University, advised by Prof. Yi Wu. I was honored to receive Yao Award (Gold Medal), the highest honor of our department. I was very fortunate to work with Prof. Yuke Zhu as a visiting researcher in UT Austin. I am broadly interested in machine learning and robotics. My research goal is to develop efficient learning methods to build autonomous robots with robust and generalizable behaviors that help humans do a wide range of real-world tasks.
Email / Twitter / Google Scholar / Linkedin |
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I am developing decision making methods for (1) enabling embodied agents to learn with minimal human supervision and limited prior domain knowledge and (2) improving the robustness and the sample efficiency of policy learning. |
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Tian Gao, Soroush Nasiriany, Huihan Liu, Quantao Yang, Yuke Zhu IEEE Robotics and Automation Letters (RA-L), 2024 project page / arXiv A primitive-based data-efficient imitation learning framework that scaffolds manipulation tasks with behavior primitives, breaking down long human demonstrations into concise, simple behavior primitive sequences. |
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Soroush Nasiriany, Tian Gao, Ajay Mandlekar, Yuke Zhu CoRL, 2022 project page / arXiv A skill-based imitation learning framework for tackling long-horizon robot manipulation tasks data-efficiently and robustly. |
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Yunfei Li*, Tian Gao*, Jiaqi Yang, Huazhe Xu, Yi Wu ICML, 2022 project page / arXiv / video A phasic self-imitative reduction (PAIR) framework for tackling sparse-reward goal-conditioned RL problems effectively. |
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