Aleksandra Faust is a Senior Staff Research Scientist and Reinforcement Learning research team co-founder at Google Brain Research. Previously, Aleksandra founded and led Task and Motion Planning research in Robotics at Google, machine learning for self-driving car planning and controls in Waymo, and was a senior researcher in Sandia National Laboratories. She earned a Ph.D. in Computer Science at the University of New Mexico (with distinction), and a Master's in Computer Science from the University of Illinois at Urbana-Champaign. Her research interests include learning for safe and scalable reinforcement learning, learning to learn, motion planning, decision-making, and robot behavior. Aleksandra won IEEE RAS Early Career Award for Industry, the Tom L. Popejoy Award for the best doctoral dissertation at the University of New Mexico in the period of 2011-2014, and was named Distinguished Alumna by the University of New Mexico School of Engineering. Her work has been featured in the New York Times, PC Magazine, ZdNet, VentureBeat, and ​was awarded Best Paper in Service Robotics at ICRA 2018, Best Paper in Reinforcement Learning for Real Life (RL4RL) at ICML 2019, and Best Paper of IEEE Computer Architecture Letters in 2020.

Latest News (last updated in 2021)

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Generalization in RL (2019 - present)

Learning to Learn for Reinforcement Learning (AutoRL) (2017 - present)

Reinforcement Learning On-Edge (2017 - present)

Past Projects

Safe reinforcement learning (2018 - 2020)

  • 2020 - Safe Policy Learning for Continuous Control, Yinlam Chow, Ofir Nachum, Aleksandra Faust, Mohammad Ghavamzadeh, Edgar Duenez-Guzman, CoRL. (Arxiv) Mentioned in Google AI Year in Review. RL4RL@ICML 2019. Best paper award.

  • 2019 - Comparison of Deep Reinforcement Learning Policies to Formal Methods for Moving Obstacle Avoidance, Arpit Garg, Hao-Tien Lewis Chiang, Satomi Sugaya, Aleksandra Faust, Lydia Tapia, to appear at IROS

  • 2018 - Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting, Aleksandra Faust, James B. Aimone, Conrad D. James, Lydia Tapia, 57th IEEE Conference on Decision and Control ZdNet Article. (Pdf, BibTex)

Self-supervision in planning (2019 - 2021)

Learning complex skills with hierarchical planning (2017 - 2022).

  • 2022 - Michael Lim, Andy Zeng, Brian Ichter, Maryam Bandari, Erwin Coumans, Claire Tomlin, Stefan Schaal, Aleksandra Faust, "Multi-Task Learning with Sequence-Conditioned Transporter Networks," ICRA 2022

  • 2021- Visual Navigation Among Humans with OptimalControl as a Supervisor, RA-L 2021, Arxiv, Website, Code. Press: VentureBeat, Techxplore.

  • 2020 - Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation, Xinlei Pan, Tingnan Zhang, Brian Ichter, Aleksandra Faust, Jie Tan, Sehoon Ha, ICRA 2022 (Arxiv)

  • 2020 - Long-Range Indoor Navigation with PRM-RL, Anthony Francis, Aleksandra Faust, Hao-Tien Lewis Chiang, Jasmine Hsu, J. Chase Kew, Marek Fiser, Tsang-Wei Edward Lee, T-RO 2020. (Citation, Arxiv, Video) Blog, Press: [1], [2], [3], [4], [5].

  • 2018 - PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning, Aleksandra Faust, Oscar Ramirez, Marek Fiser, Kenneth Oslund, Anthony Francis, James Davidson, Lydia Tapia, ICRA. Best paper in Service Robotics; Mentioned in Looking Back at Google’s Research Efforts in 2018, Blog. (pdf, Bibtex, Video)

  • 2018 - FollowNet: Towards Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning, Pararth Shah, Marek Fiser, Aleksandra Faust, J. Chase Kew, Dilek Hakkani-Tur, 3rd MLPC at ICRA, May 2018 (Pdf, BibTex)