The 4th Conference on Robot Learning (CoRL) today announced its Best Paper and Best System Paper Awards. The Best Paper Award went to Learning Latent Representations to Influence Multi-Agent Interaction, and the paper SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving won the Best System Paper Award.
Since launching in 2017, CoRL has quickly become one of the world’s top academic gatherings at the intersection of robotics and machine learning: “a selective, single-track conference for robot learning research, covering a broad range of topics spanning robotics, ML and control, and including theory and applications.”
CoRL 2020 runs virtually through November 18. This year saw 475 papers submitted, a 20 percent rise over 2019. There were 165 papers accepted for a 34.7 percent acceptance rate, up slightly from last year’s 27.6 percent.
Best System Paper Award Winner
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
- Institutions: Noah’s Ark Lab, Huawei Technologies; Shanghai Jiao Tong University; University College London
- Authors: Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Aurora Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Dong Chen, Zhengbang Zhu, Nhat Nguyen, Mohamed Elsayed, Kun Shao, Sanjeevan Ahilan, Baokuan Zhang, Jiannan Wu, Zhengang Fu, Kasra Rezaee, Peyman Yadmellat, Mohsen Rohani, Nicolas Perez Nieves, Yihan Ni, Seyedershad Banijamali, Alexander Cowen Rivers, Zheng Tian, Daniel Palenicek, Haitham bou Ammar, Hongbo Zhang, Wulong Liu, Jianye Hao, Jun Wang
DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer
- Institutions: University of California, Berkeley
- Author: Ajay Kumar Tanwani
Best Paper Award Winner
Learning Latent Representations to Influence Multi-Agent Interaction
- Institutions: Stanford University, Virginia Tech
- Authors: Annie Xie, Dylan P. Losey, Ryan Tolsma, Chelsea Finn, Dorsa Sadigh
Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions
- Institutions: University of California, Berkeley; California Institute of Technology
- Authors: Sarah Dean, Andrew J. Taylor, Ryan K. Cosner, Benjamin Recht, Aaron D. Ames
Learning from Suboptimal Demonstration via Self-Supervised Reward Regression
- Institutions: Georgia Institute of Technology
- Authors: Letian Chen, Rohan Paleja, Matthew Gombolay
Safe Optimal Control Using Stochastic Barrier Functions and Deep Forward-Backward SDEs
- Institutions: Georgia Institute of Technology, Stanford University
- Authors: Marcus Aloysius Pereira, Ziyi Wang, Ioannis Exarchos, Evangelos A. Theodorou
All CoRL 2020 paper presentation videos and livestreams can be found on the CoRL YouTube channel.
Reporter: Fangyu Cai | Editor: Michael Sarazen
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