Speakers
Hadas Kress-Gazit
Hadas Kress-Gazit is a Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University. She received her Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania in 2008 and has been at Cornell since 2009. Her research focuses on formal methods for robotics and automation and more specifically on synthesis for robotics – automatically creating verifiable robot controllers for complex high-level tasks. Her group explores different types of robotic systems including modular robots, soft robots and swarms and synthesizes (pun intended) ideas from different communities such as robotics, formal methods, control, hybrid systems and computational linguistics. She is an IEEE fellow and has received multiple awards for her research, teaching and advocacy for groups traditionally underrepresented in STEM. She lives in Ithaca with her partner and two kids.
Peter Stone
Peter Stone is the founder and director of the Learning Agents Research Group (LARG) within the Artificial Intelligence Laboratory in the Department of Computer Science at The University of Texas at Austin, as well as associate department chair and Director of Texas Robotics. Peter was a co-founder of Cogitai, Inc. and am now Executive Director of Sony AI America. His main research interest in AI is understanding how we can best create complete intelligent agents. He considers adaptation, interaction, and embodiment to be essential capabilities of such agents. Thus, his research focuses mainly on machine learning, multiagent systems, and robotics. The most exciting research topics to Peter are those inspired by challenging real-world problems. He believes that complete successful research includes both precise, novel algorithms and fully implemented and rigorously evaluated applications. His application domains have included robot soccer, autonomous bidding agents, autonomous vehicles, and human-interactive agents.
Jesse Thomason
Jesse Thomason is an assistant professor at University of Southern California. He leads the GLAMOR Lab at USC. His research brings together natural language processing and robotics to connect language to the world (RoboNLP). Jess is interested in connecting language to agent perception and action, and lifelong learning through interaction.
David Abel
David Abel is a Senior Research Scientist at DeepMind in London. Before that, I completed my Ph.D in Computer Science at Brown University advised by Prof. Michael Littman. David got his start in research working with Prof. Stefanie Tellex during his Masters in CS at Brown, where he also completed a Masters in Philosophy working with Prof. Joshua Schechter. His research focuses on bringing clarity to the central philosophical questions surrounding agency, computation, and learning. David values research that provides new understanding, and tend to get excited by simple but foundational questions. He typically work with the reinforcement learning problem, drawing on tools and perspectives from across philosophy, math, and computer science. He is currently interested in better defining the main concepts of AI, such as learning, agency, and goals. Previously, his dissertation studied how agents model the worlds they inhabit, focusing on the representational practices that underly effective learning and planning.
Dagmar Sternad
The central interest of research in the Action Lab is the control and coordination of goal-directed human behavior. What organizational principles are at work when generating functional perceptually guided movements? The theoretical framework that pervades our studies interprets the actor in the environment as a dynamical system, which is high-dimensional, nonlinear, and capable of producing coordinated and adaptive behavior. More specifically, Prof. Sternad’s research agenda focuses on single- and multi- joint human movements in perceptually specified tasks. Her lab pursues a three-pronged research strategy consisting of: (1) an empirical component with behavioral experiments on human subjects, (2) theoretical work which develops mathematical models for movement generation on the basis of coupled dynamical systems, and (3) brain imaging studies that investigate the cerebral activity accompanying movement. More recently, Prof. Sternad’s lab has extended these experimental paradigms to neurological disorders such as Parkinson’s disease and the elderly.
Cédric Colas
Cédric Colas is interested in the study of artificial open-ended skill discovery, the ability for an artificial agent to grow an open-ended repertoire of skills along its life. This led me to focus on intrinsically motivated agents that set their own goals, autotelic agents.
Cédric is also convinced that to develop such abilities, agents will need to be immersed into rich socio-cultural worlds, to interact with their peers and with humans, to participate in a shared cultural evolution, with us. This idea is developed in our perspective article.
Cédric obtained my PhD at the Flowers Lab under the supervision of Pierre-Yves Oudeyer and Olivier Sigaud. The title of the thesis is Towards Vygotskian Autotelic Agents: Learning Skills with Goals, Language and Intrinsically Motivated Deep Reinforcement Learning (manuscript, defense).
Cédric is currently working with Joshua Tenenbaum and Jacob Andreas at MIT, where Cédric develops autotelic agents able to learn from humans and others using program synthesis methods.