Dieter Fox is the head of the UW Robotics and State Estimation Lab RSE-Lab. He is also a Senior Director of Robotics Research at Nvidia. His research is in robotics, with strong connections to artificial intelligence, computer vision, and machine learning. 

Fei-Fei Li is the inaugural Sequoia Professor in the Computer Science Department at Stanford University, and Co-Director of Stanford’s Human-Centered AI Institute. Her current research interests include cognitively inspired AI, machine learning, deep learning, computer vision and AI+healthcare especially ambient intelligent systems for healthcare delivery.

Florian Shkurti is an assistant professor in computer science at the University of Toronto, focusing on robotics, machine learning, and vision. His research group develops methods that enable robots to perceive, reason, plan, and act effectively and safely, particularly in dynamic environments and alongside humans. Application areas of his research include field robotics for environmental monitoring, visual navigation for autonomous vehicles, and mobile manipulation.

Hector Geffner is an Alexander von Humboldt Professor at the RWTH Aachen University, Germany and a Guest Wallenberg Professor at Linköping University, Sweden. Before joining RWTH, he was an ICREA Research Professor at the Universitat Pompeu Fabra (UPF) in Barcelona, Spain. Hector obtained a Ph.D. in Computer Science at UCLA and then worked at the IBM T.J. Watson Research Center in NY, and at the Universidad Simon Bolivar in Caracas. Distinctions for his work include the 1990 ACM Dissertation Award and three ICAPS Influential Paper Awards. He currently leads a project on representation learning for acting and planning (RLeap), funded by an Advanced ERC grant, where he has been studying the problemof learning different type of structures needed to act and plan like action models, general policies, general subgoal structures, and hierarchical policies.

Marc Toussaint is a professor in the area of AI & Robotics at TU Berlin. His research interests are in the intersection of AI and robotics, namely in using Machine Learning, optimization, and AI reasoning to tackle fundamental problems in robotics. He work on models and algorithms for physical reasoning, task-and-motion planning (logic-geometric programming), learning heuristics, the planning-as-inference paradigm, algorithms and methods for robotic building construction, and learning to transfer model-based strategies to reactive and adaptive real-world behavior.

Masataro Asai is a Research Staff Member at IBM Research Cambridge (MIT-IBM Watson AI Lab) after receiving a Ph.D from University of Tokyo in 2018 under Alex Fukunaga and working for IBM Research Tokyo during 2019. His main expertise is Classical Planning and Heuristic Graph Search, while his recent work focuses on the automatic identification of discrete symbolic entities that aids planning, i.e., symbol grounding, with the help of Deep Neural Networks.

Pratyusha Sharma is a PhD student in EECS at MIT advised by Prof. Antonio Torralba and Prof. Jacob Andreas. Her research goal is to understand what can be learnt from rich multimodal interactions (vision, touch and sounds) with objects (and people) in the world around us. She is also interested in developing systems that enable robots to efficiently abstract knowledge across tasks, reason, understand goals and reliably interact in the real world.

Russ Tedrake is the Toyota Professor of Electrical Engineering and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT. He is interested in finding elegant control solutions for interesting (underactuated, stochastic, and/or difficult to model) dynamical systems that he can build and experiment with. He is particularly interested in finding connections between mechanics (especially non-smooth mechanics) and optimization theory which enable formal analysis and control design for complex mechanical systems. These days He is primarily interested in bringing the rigor of systems theory to robot manipulation.

Siddharth Srivastava is an assistant professor at Arizona State University. His research focuses on learning generalizable knowledge for reliable sequential decision making, AI safety, and AI assessment. He is a recipient of the NSF CAREER award, a Best Paper award at the International Conference on Automated Planning and Scheduling (ICAPS) and an Outstanding Dissertation award from the Department of Computer Science at UMass Amherst. His work on TAMP focuses on well-founded algorithms for using abstractions for integrated task and motion planning, with an emphasis on learning transferrable state and action hierarchies.