Tomás Lozano-Pérez

Affiliations: 
1981- Computer Science Massachusetts Institute of Technology, Cambridge, MA, United States 
Area:
robotics, computer vision, machine learning, medical imaging, and computational chemistry.
Website:
http://people.csail.mit.edu/tlp/
Google:
"Tomás Lozano-Pérez"
Bio:

Tomas Lozano-Perez is currently the School of Engineering Professor in Teaching Excellence at the Massachusetts Institute of Technology (MIT), USA, where he is a member of the Computer Science and Artificial Intelligence Laboratory. He has been Associate Director of the Artificial Intelligence Laboratory and Associate Head for Computer Science of MIT?s Department of Electrical Engineering and Computer Science. He was a recipient of the 2021 IEEE Robotics and Automation Award, the 2011 IEEE Robotics Pioneer Award and a 1985 Presidential Young Investigator Award. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Fellow of the ACM and a Fellow of the IEEE.

Cross-listing: Neurotree

Parents

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Berthold K. P. Horn grad student MIT (Neurotree)

Children

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John F. Canny grad student MIT
Bruce R. Donald grad student MIT
Matthew T. Mason grad student
Daniel S. Weld grad student 1984-1988 MIT (Computer Science Tree)
Michael A. Erdmann grad student 1989 MIT (Computer Science Tree)
Paul A Viola grad student 1995 MIT (LinguisTree)
Lisa C. Tucker-Kellogg grad student 1997-2002 MIT (Neurotree)
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Publications

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Kim B, Kaelbling LP, Lozano-Pérez T. (2019) Adversarial Actor-Critic Method for Task and Motion Planning Problems Using Planning Experience Proceedings of the Aaai Conference On Artificial Intelligence. 33: 8017-8024
Kim B, Wang Z, Kaelbling LP, et al. (2019) Learning to guide task and motion planning using score-space representation The International Journal of Robotics Research. 38: 793-812
Konidaris G, Kaelbling LP, Lozano-Perez T. (2018) From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning Journal of Artificial Intelligence Research. 61: 215-289
Garrett CR, Lozano-Pérez T, Kaelbling LP. (2018) Sampling-based methods for factored task and motion planning The International Journal of Robotics Research. 37: 1796-1825
Axelrod B, Kaelbling LP, Lozano-Pérez T. (2018) Provably safe robot navigation with obstacle uncertainty The International Journal of Robotics Research. 37: 1760-1774
Garrett CR, Lozano-Pérez T, Kaelbling LP. (2017) FFRob: Leveraging symbolic planning for efficient task and motion planning The International Journal of Robotics Research. 37: 104-136
Lee G, Lozano-Pérez T, Kaelbling LP. (2015) Hierarchical planning for multi-contact non-prehensile manipulation Ieee International Conference On Intelligent Robots and Systems. 2015: 264-271
Garrett CR, Lozano-Pérez T, Kaelbling LP. (2015) FFRob: An efficient heuristic for task and motion planning Springer Tracts in Advanced Robotics. 107: 179-195
Frazzoli E, Lozano-Pérez T, Roy N, et al. (2014) Special Issue on the Tenth International Workshop on Algorithmic Foundations of Robotics (WAFR) International Journal of Robotics Research. 33: 3-4
Lozano-Pérez T, Kaelbling LP. (2014) A constraint-based method for solving sequential manipulation planning problems Ieee International Conference On Intelligent Robots and Systems. 3684-3691
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