Michelle A. Lee

According to our database, Michelle A. Lee authored at least 10 papers between 2019 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation.

Hao Li, Yizhi Zhang, Junzhe Zhu, Shaoxiong Wang, Michelle A. Lee, Huazhe Xu, Edward H. Adelson, Li Fei-Fei, Ruohan Gao, Jiajun Wu

Proceedings of the Conference on Robot Learning, 2022

2021

MultiBench: Multiscale Benchmarks for Multimodal Representation Learning.

Paul Pu Liang, Yiwei Lyu, Xiang Fan, Zetian Wu, Yun Cheng, Jason Wu, Leslie Chen, Peter Wu, Michelle A. Lee, Yuke Zhu, Ruslan Salakhutdinov, Louis-Philippe Morency

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Differentiable Factor Graph Optimization for Learning Smoothers.

Brent Yi, Michelle A. Lee, Alina Kloss, Roberto Martín-Martín, Jeannette Bohg

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021

Interpreting Contact Interactions to Overcome Failure in Robot Assembly Tasks.

Peter A. Zachares, Michelle A. Lee, Wenzhao Lian, Jeannette Bohg

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Detect, Reject, Correct: Crossmodal Compensation of Corrupted Sensors.

Michelle A. Lee, Matthew Tan, Yuke Zhu, Jeannette Bohg

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020

Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks.

Michelle A. Lee, Yuke Zhu, Peter Zachares, Matthew Tan, Krishnan Srinivasan, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg

IEEE Trans. Robotics, 2020

Multimodal Sensor Fusion with Differentiable Filters.

Michelle A. Lee, Brent Yi, Roberto Martín-Martín, Silvio Savarese, Jeannette Bohg

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning.

Michelle A. Lee, Carlos Florensa, Jonathan Tremblay, Nathan D. Ratliff, Animesh Garg, Fabio Ramos, Dieter Fox

Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

2019

Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks.

Roberto Martín-Martín, Michelle A. Lee, Rachel Gardner, Silvio Savarese, Jeannette Bohg, Animesh Garg

Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019

Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks.

Michelle A. Lee, Yuke Zhu, Krishnan Srinivasan, Parth Shah, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg

Proceedings of the International Conference on Robotics and Automation, 2019