About Me

Hi! I am a third year PhD candidate in the Stanford AI Lab, interested in robot learning, perception, and controls.

Email: michellelee@cs.stanford.edu

Twitter: michellearning

Gates Computer Science Building, Room 132
353 Serra Mall, Stanford University
Stanford, CA 94305-9025, USA

News

One paper accepted in ICRA 2020

Our journal version of the Multimodal Representation paper has been accepted to T-RO

Our paper Guided Uncertainty Aware Policy Optimization won best paper at the Robot Learning workshop at NeurIPS 2019.

One paper accepted in IROS 2019.

We won the Best Conference Paper Award at ICRA 2019 in Montreal. We also were the Best Award in Cognitive Robotics Finalist.

Publications

  1. Lee, M. A., Zhu, Y., Zachares, P., Tan, M., Srinivasan, K., Savarese, S., Fei-Fei, L., Garg, A., & Bohg, J. (2019). Making sense of vision and touch: Learning multimodal representations for contact-rich tasks. IEEE Transactions on Robotics.

  2. Martín-Martín, R., Lee, M. A., Gardner, R., Savarese, S., Bohg, J., & Garg, A. (2019). Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE.

  3. Lee, M. A*, Zhu, Y.*, Srinivasan, K., Shah, P., Savarese, S., Fei-Fei, L., Garg, A., & Bohg, J. (2019). Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks. In 2019 IEEE International Conference on Robotics and Automation (ICRA). IEEE.

  4. Lee, M. A.*, Florensa, C.*, Tremblay, J., Ratliff, N., Garg, A., Ramos, F., & Fox, D. (2020). Guided Uncertainty Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning. In 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE.


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