Aleksandra Faust is a Staff Research Scientist at Google Brain Research, specializing in reinforcement learning and motion planning. Previously, Aleksandra led Task and Motion Planning research in Robotics at Google, machine learning 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 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 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.
Oct 2020 - Two papers accepted to CoRL.
Jun 2020 - Neural Collision Clearance Estimator for Batched Robot Motion Planning accepted to WAFR.
May 2020 - Fast Deep Swept Volume Estimator accepted to IJRR.
Apr 2020 - Long-Range Indoor Navigation with PRM-RL accepted to T-RO.
Mar 2020 - VentureBeat: Google’s AI helps robots navigate around humans in offices
Jan 2020 - Research featured in Google Research: Looking Back at 2019, and Forward to 2020 and Beyond
Nov 2019 - Interview with GA Tech
Learning to Learn for Reinforcement Learning (AutoRL)
2019 - Evolving Rewards to Automate Reinforcement Learning, Aleksandra Faust, Anthony Francis, Dar Mehta, 6th AutoML@ICML. (Arxiv, BibTex) 58% acceptance rate, Mentioned in Jeff Dean's keynote. Press: .
2019 - Learning Navigation Behaviors End to End with AutoRL, Hao-Tien Chiang, Aleksandra Faust, Marek Fiser, Antony Francis, IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 2007-2014, April 2019. Blog, Press: , , , , . #1 or #2 (out of ~1600) most downloaded paper in Feb-Sep 2019. (Preprint, BibTex, Video)
Self-supervision in planning
2020 - Fast Deep Swept Volume Estimator, Hao-Tien Lewis Chiang, John E. G. Baxter, Satomi Sugaya, Mohammad R. Yousefi, Aleksandra Faust, Lydia Tapia, The International Journal of Robotics Research (IJRR).
2020 - Cooperation without Coordination: Hierarchical Predictive Planning for Decentralized Multiagent Navigation, Rose E. Wang, J. Chase Kew, Dennis Lee, Brian Ichter, Tsang-Wei Edward, Lee, Tingnan Zhang, Jie Tan, Aleksandra Faust, Arxiv, website, video
2020 - Neural Collision Clearance Estimator for Batched Robot Motion Planning, J. Chase Kew, Brian Ichter, Maryam Bandari, Tsang-Wei Edward Lee, Aleksandra Faust, WAFR. (Arxiv)
2019 - RL-RRT: End-to-End Kinodynamic Robot Motion Planning, Hao-Tien Lewis Chiang, Jasmine Hsu, Marek Fiser, Lydia Tapia, Aleksandra Faust, IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 4298-4305, Oct. 2019. (Paper and citation, Video) #17 out of ~1600 most downloaded paper in Sep 2019.
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)
Learning complex skills with hierarchical planning
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 to appear (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: , , , , .
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)
Reinforcement Learning On-Edge
2020 - The Sky Is Not the Limit: A Visual Performance Model for Cyber-Physical Co-Design in Autonomous Machines, Srivatsan Krishnan, Zishen Wan, Kshitij Bhardwaj, Paul Whatmough, Aleksandra Faust, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, CA-L, paper.
2020- Air Learning: An End-to-end Learning Gym For Aerial Robots, Srivatsan Krishnan, Colby Banbury, Bardienus Duisterhof, Aleksandra Faust, Vijay Janapa Reddi, MLSyS Demo Track (36% acceptance rate)
2019 - Quantized Reinforcement Learning (QUARL), Srivatsan Krishnan, Sharad Chitlangia, Maximilian Lam, Zishen Lam, Aleksandra Faust, Vijay Janapa Reddi. 1st Workshop on Resource-Constrained Machine Learning. Medium (Arxiv, GitHub)
2019 - Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller, Bardienus P. Duisterhof, Srivatsan Krishnan, Jonathan J. Cruz, Colby R. Banbury, William Fu, Aleksandra Faust, Guido C. H. E. de Croon, Vijay Janapa Reddi. BitCraze blog (Arxiv, Video, GitHub)
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)
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)