Aleksandra Faust is a Staff Research Scientist at Google Brain Robotics, specializing in robot motion planning and reinforcement learning. Previously, Aleksandra led machine learning efforts for self-driving car planning and controls in Waymo, and was a 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 machine learning for safe, scalable, and socially-aware motion planning, decision-making, and robot behavior. Aleksandra won the Tom L. Popejoy Award for the best doctoral dissertation at the University of New Mexico in Engineering, Mathematics, and Sciences 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, and ​was awarded Best Paper in Service Robotics at ICRA 2018 and Best Paper in Reinforcement Learning for Real Life (RL4RL) at ICML.

Learning basic skills

AutoRL: Evolution + RL for better robot skills

  • 2019 - Evolving Rewards to Automate Reinforcement Learning, Aleksandra Faust, Anthony Francis, Dar Mehta, 6th AutoML@ICML. 58% acceptance rate, Mentioned in Jeff Dean's keynote. (Arxiv, BibTex)
  • 2019 - Learning Navigation Behaviors End to End with AutoRL, Hao-Tien Chiang, Aleksandra Faust, Marek Fiser, Antony Francis, IEEE Robotics and Automation Letters/ICRA. Blog, Press: [1], [2], [3], [4], [5]. (Preprint, BibTex, Video)

Safe reinforcement learning

  • 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
  • 2019 - Lyapunov-based Safe Policy Optimization for Continuous Control, Yinlam Chow, Ofir Nachum, Aleksandra Faust, Mohammad Ghavamzadeh, Edgar Duenez-Guzman, RL4RL@ICML 2019. Best paper award. (Arxiv)
  • 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)

Learning complex skills with hierarchical planning

Navigation with Deep RL

  • 2019 - 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. Blog, Press: [1], [2], [3], [4], [5]. (Arxiv, Bibtex, Video)
  • 2019 - Learning to Navigate the Web, Izzeddin Gur, Ulrich Rueckert, Aleksandra Faust, Dilek Hakkani-Tur, ICLR. ZdNet article; Top 10%, 30% acceptance rate. (Pdf, BibTex)
  • 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)

Learning skill performance estimators for better planning.

  • 2019 - RL-RRT: End-to-End Kinodynamic Robot Motion Planning, Hao-Tien Lewis Chiang, Jasmine Hsu, Marek Fiser, Lydia Tapia, Aleksandra Faust, to appear RA-L
  • 2019 - Learned Critical Probabilistic Roadmaps for Robotic Motion Planning, Brian Ichter, Aleksandra Faust, Topological Methods in Robot Planning@ICRA (Paper)
  • 2018 - Fast Swept Volume Estimation with Deep Learning, Hao-Tien Chiang, Aleksandra Faust, Satomi Sugaya and Lydia Tapia, WAFR Mentioned in Looking Back at Google’s Research Efforts in 2018. (Pdf, BibTex)
  • 2018 - A Deep Neural Network for Swept Volume Prediction Between Configurations, Hao-Tien Chiang, Aleksandra Faust, Lydia Tapia, 3rd MLPC at ICRA, (Pdf, Bibtex).

Make it work on real robots

Learning On-Edge: Deep RL that works with limited compute

  • 2019 - Air Learning: An AI Research Platform for Algorithm-Hardware Benchmarking of Autonomous Aerial Robots, Srivatsan Krishnan, Behzad Borojerdian, William Fu, Aleksandra Faust, Vijay Janapa Reddi. (Arxiv, GitHub)
  • 2019 -Toward Exploring End-to-End Learning Algorithms for Autonomous Aerial Machines, Srivatsan Krishnan, Behzad Boroujerdian, Aleksandra Faust, Vijay Janapa Reddi, LLAF@ICRA 2019 (Paper)
  • 2018 - MAVBench: Micro Aerial Vehicle Benchmarking,” 51st IEEE/ACM International Symposium on Microarchitecture (MICRO), Behzad Boroujerdian, Hasan Genc, Srivatsan Krishnan, Wenzhi Cui, Aleksandra Faust, Vijay Janapa Reddi, 21% acceptance rate. (Pdf, BibTex, Video)
  • 2018 - Why Compute Matters for UAV Energy Efficiency?, Behzad Boroujerdian, Hasan Genc, Srivatsan Krishnan, Aleksandra Faust, Vijay Janapa Reddi, 2nd International Symposium on Aerial Robotics. (Pdf, BibTex)