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Object Representations for Learning and Reasoning
William Agnew · Rim Assouel · Michael Chang · Antonia Creswell · Eliza Kosoy · Aravind Rajeswaran · Sjoerd van Steenkiste

Fri Dec 11 08:00 AM -- 07:15 PM (PST) @
Event URL: https://orlrworkshop.github.io/ »

Recent advances in deep reinforcement learning and robotics have enabled agents to achieve superhuman performance on a variety of challenging games and learn complex manipulation tasks. While these results are very promising, several open problems remain. In order to function in real-world environments, learned policies must be both robust to input perturbations and be able to rapidly generalize or adapt to novel situations. Moreover, to collaborate and live with humans in these environments, the goals and actions of embodied agents must be interpretable and compatible with human representations of knowledge. Hence, it is natural to consider how humans so successfully perceive, learn, and plan to build agents that are equally successful at solving real world tasks.
There is much evidence to suggest that objects are a core level of abstraction at which humans perceive and understand the world [8]. Objects have the potential to provide a compact, casual, robust, and generalizable representation of the world. Recently, there have been many advancements in scene representation, allowing scenes to be represented by their constituent objects, rather than at the level of pixels. While these works have shown promising results, there is still a lack of agreement on how to best represent objects, how to learn object representations, and how best to leverage them in agent training.
In this workshop we seek to build a consensus on what object representations should be by engaging with researchers from developmental psychology and by defining concrete tasks and capabilities that agents building on top of such abstract representations of the world should succeed at. We will discuss how object representations may be learned through invited presenters with expertise both in unsupervised and supervised object representation learning methods. Finally, we will host conversations and research on new frontiers in object learning.

Author Information

William Agnew (University of Washington)
Rim Assouel (umontreal.ca)
Michael Chang (University of California, Berkeley)

Ph.D. student at Berkeley AI Research, U.C. Berkeley B.S. in Computer Science from MIT Former research intern under Juergen Schmidhuber, Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA) Former undergraduate researcher under Joshua Tenenbaum and Antonio Torralba, MIT

Antonia Creswell (Deep Mind)

Antonia Creswell is a Senior Research Scientist at DeepMind in the Cognition team. Her work focuses on the learning and integration of object representations in dynamic models. She completed her PhD on representation learning at Imperial College London in the department of Bioengineering.

Eliza Kosoy (UC Berkeley)

Working with Alison Gopnik (and a few others at MIT and Berkeley) on the intersections of ai and child development

Aravind Rajeswaran (University of Washington)
Sjoerd van Steenkiste (Dalle Molle Institute for Artificial Intelligence Research (IDSIA))

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