Fan Yang

I am a PhD student in robotics at University of Michigan, Ann Arbor advised by Prof. Dmitry Berenson. I am working on robot manipulation. Previously, I was a master's student in robotics (MSR) at Robotics Institute of Carnegie Mellon University, where I am advised by Prof. David Held. I was working on safe Reinforcement Learning and manipualtion with Prof. Held.

Prior to Joining CMU, I was an undergraduate student at Tsinghua University majoring in Engineering Mechanics (Qian class). I was also fortunate to have a chance to be advised by Prof. Richard Voyles, Prof. Oussama Khatib, Prof. Masayoshi Tomizuka, and Prof. Huaping Liu.

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We propose a spatially-grounded and temporally-abstracted action representation with a hybrid discrete-continuous reinforcement learning framework.

Keywords: RL with 3D Vision, Action Representation, Contact-rich manipulation

The method of graduating incurring a harder safety constraints can lead to a better performance in safe RL tasks.

We develop a benchmark and evaluate the state-of-the-art lifelong learning method on reinforcement learning tasks, especially robotic manipulation tasks.

An adversarial training algorithm is used to increase the robustness of robot joint damage.

We develop a method that uses RL agent to select important interactions for trajectory prediction in a highly interactive environment.

We develop a Graph-Neural-Network-based method captures the interactions between different agents for trajectory prediction in a highly interactive environment.