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Autonomous agents must work with and around humans and other AI agents to automate difficult tasks, and perform complex planning and control tasks. Learning and working in environments where humans are present poses a number of safety and data-impoverished challenges. In the first part of the talk, we discuss data augmentation, reasoning, and learning methods as means to overcome the challenges of learning in human environments. The second part of the talk discusses methods and applications where autonomous agents work in lieu of humans, and learn how to design agents that perform complex tasks such as navigating the web or designing ML systems. This paradigm gives rise to two team cooperative systems, designers and agents under-training. We discuss several paradigms, including learning generative environments and multi-agent decomposition of interconnected systems. We close with the look into the future and discuss the role of the emerging work in foundation models.
Dr. Aleksandra Faust is a Senior Staff Research Scientist, Autonomous Agents research lead, and Reinforcement Learning research team co-founder at Google Brain. Her research is centered around safe and scalable autonomous systems for social good, including reinforcement learning, planning, and control for robotics, autonomous driving, and digital assistants. 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. Aleksandra won the 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, Best Paper of IEEE Computer Architecture Letters in 2020, and IEEE Micro Top Picks 2023 Honorable Mention.