Jiawei Gao「高嘉伟」

I am a senior undergraduate student in Department of Automation, Tsinghua University. I am also a member of the Tong Class , an AGI program founded by Prof. Song-Chun Zhu.

I am very fortuante to have worked with Prof. Gao Huang. and Prof. Yilin Mo at Tsinghua University. Currently, I am a research intern at Shanghai AI Laboratory, advised by Prof. Jiangmiao Pang.

Email: winstongu20[AT]gmail[DOT]com

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In the rapidly advancing and exciting field of robotics, my research is focused on developing robot systems that possess interpretability, robustness, agility, efficiency, and maybe even further, with intelligence and grace. I believe that integrating learning with control as learning-based controllers is essential for realizing these traits. Thererfore, I seek to explore ways to utilizing the structural insights of system dynamics provided by control theory, to establish an optimized basis for learning algorithms.

To go even further, I believe that combining control and learning into a unified framework will not only result in robust learning-based controllers but also revolutionize people's perceptions of intelligence. The intelligence of so called "embodied agents" in the future might look similar to systems that perceive their environments as inputs andgenerate corresponding outputs, similar to actions. Moreover, principles such as stability, controllability, and observability may become essential in analyzing how these agents perceive environments, decide, store and retrieve memories, and take actions.

Furthermore, I am intrigued by the idea of applying physics-based character animation techniques to the learning process of humanoid robots. I think it would be very amusing to watch humanoid robots move and behave like animated characters from movies or video games.

We present the Hybrid Internal Model, a method enabling the control policy to estimate environmental disturbances by only explicitly estimating velocity and implicitly simulating the system's response.

We propose FamO2O, a simple yet effective framework that empowers existing offline-to-online RL algorithms to determine state-adaptive improvement-constraint balances. FamO2O utilizes a universal model to train a family of policies with different improvement/constraint intensities, and a balance model to select a suitable policy for each state.

Updated at Jan. 2024.
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