Elizabeth Spelke is the Marshall L. Berkman Professor of Psychology at Harvard University and an investigator at the NSF-MIT Center for Brains, Minds and Machines. Her laboratory focuses on the sources of uniquely human cognitive capacities, including capacities for formal mathematics, for constructing and using symbols, and for developing comprehensive taxonomies of objects. She probes the sources of these capacities primarily through behavioral research on human infants and preschool children, focusing on the origins and development of their understanding of objects, actions, people, places, number, and geometry. In collaboration with computational cognitive scientists, she aims to test computational models of infants’ cognitive capacities. In collaboration with economists, she has begun to take her research from the laboratory to the field, where randomized controlled experiments can serve to evaluate interventions, guided by research in cognitive science, that seek to enhance young children’s learning.

How can we obtain object representations in real world environments? How can object representations be applied in robotics? Join us for a panel discussion with Jessica Hamrick, Irina Higgins, Michelle Lee, Josh Tenenbaum, moderated by Klaus Greff.

Jessica Hamrick is a Senior Research Scientist at DeepMind, where she studies how to build machines that can flexibly build and deploy models of the world. Her work combines insights from cognitive science with structured relational architectures, model-based deep reinforcement learning, and planning. Jessica received a Ph.D. in Psychology from UC Berkeley in 2017, and an M.Eng. and B.S. in Computer Science from MIT in 2012.

Irina Higgins is a research scientist at DeepMind, where she works in the Frontiers team. Her work aims to bring together insights from the fields of neuroscience and physics to advance general artificial intelligence through improved representation learning. Before joining DeepMind, Irina was a British Psychological Society Undergraduate Award winner for her achievements as an undergraduate student in Experimental Psychology at Westminster University, followed by a DPhil at the Oxford Centre for Computational Neuroscience and Artificial Intelligence, where she focused on understanding the computational principles underlying speech processing in the auditory brain. During her DPhil, Irina also worked on developing poker AI, applying machine learning in the finance sector, and working on speech recognition at Google Research.

Sungjin Ahn is an Assistant Professor of Computer Science at Rutgers University and directs the Rutgers Machine Learning (RUML) lab. He is also affiliated with Rutgers Center for Cognitive Science. His research focus is on how an AI-agent can learn the structure and representations of the world in an unsupervised and compositional way, with a particular interest in object-centric learning. His approach to achieving this is based on deep learning, Bayesian modeling, reinforcement learning, and inspiration from cognitive & neuroscience. He received Ph.D. at the University of California, Irvine with Max Welling and did a postdoc with Yoshua Bengio at Mila. Then, he joined Rutgers University in Fall 2018. He has co-organized ICML 2020 Workshop on Object-Oriented Learning and received the ICML best paper award in ICML 2012.

Wilka Carvalho is a PhD Candidate in Computer Science at the University of Michigan–Ann Arbor where he is advised by Honglak Lee, Satinder Singh, and Richard Lewis. His long-term research goal is to develop cognitive theories of learning that help us understand how humans infer, reason with, and exploit the rich structure present in realistic visual scenes to enable sophisticated behavioral policies. Towards this end, he is studying how object-centric representation learning and reinforcement learning can bring us closer to human-level artificial intelligence. He is supported by an NSF GRFP Fellowship and a UM Rackham Merit Fellowship.

Renée Baillargeon is an Alumni Distinguished Professor of Psychology at the University of Illinois Urbana-Champaign. Her research examines cognitive development in infancy and focuses primarily on causal reasoning. In particular, she explores how infants make sense of the events they observe, and what explanatory frameworks and learning mechanisms enable them to do so. In addition to this primary focus on causal reasoning, she is interested in a broad range of related issues including object perception, categorization, object individuation, number, and executive-function skills.

Object Representations for Robot Manipulation

Reasoning about objects is a fundamental task in robot manipulation. Different representations can have important repercussions on the capabilities and generality of a manipulation system. In this talk I will discuss different ways we represent and reason about objects, ranging from explicit 3D models to raw point clouds.

What should be in an object representation, what should an object representation be able to do, and how do we measure and compare them? Join us for a panel discussion with Wilka Carvalho, Judy Fan, Tejas Kulkarni, and Chris Xie, moderated by Rachit Dubey.