Workshop
Modeling the Physical World: Learning, Perception, and Control
Jiajun Wu · Kelsey Allen · Kevin Smith · Jessica Hamrick · Emmanuel Dupoux · Marc Toussaint · Josh Tenenbaum

Fri Dec 7th 08:00 AM -- 06:30 PM @ Room 517 C
Event URL: http://phys2018.csail.mit.edu/ »

Despite recent progress, AI is still far from achieving common-sense scene understanding and reasoning. A core component of this common sense is a useful representation of the physical world and its dynamics that can be used to predict and plan based on how objects interact. This capability is universal in adults, and is found to a certain extent even in infants. Yet despite increasing interest in the phenomenon in recent years, there are currently no models that exhibit the robustness and flexibility of human physical reasoning.

There have been many ways of conceptualizing models of physics, each with their complementary strengths and weaknesses. For instance, traditional physical simulation engines have typically used symbolic or analytic systems with “built-in” knowledge of physics, while recent connectionist methods have demonstrated the capability to learn approximate, differentiable system dynamics. While more precise, symbolic models of physics might be useful for long-term prediction and physical inference; approximate, differentiable models might be more practical for inverse dynamics and system identification. The design of a physical dynamics model fundamentally affects the ways in which that model can, and should, be used.

This workshop will bring together researchers in machine learning, computer vision, robotics, computational neuroscience, and cognitive psychology to discuss artificial systems that capture or model the physical world. It will also explore the cognitive foundations of physical representations, their interaction with perception, and their applications in planning and control. There will be invited talks from world leaders in the fields, presentations and poster sessions based on contributed papers, and a panel discussion.

Topics of discussion will include
- Building and learning physical models (deep networks, structured probabilistic generative models, physics engines)
- How to combine model-based and model-free approaches to physical prediction
- How to use physics models in higher-level tasks such as navigation, video prediction, robotics, etc.
- How perception and action interact with physical representations
- How cognitive science and computational neuroscience may inform the design of artificial systems for physical prediction
- Methodology for comparing models of infant learning with artificial systems
- Development of new datasets or platforms for physics and visual common sense

08:40 AM Opening Remarks: Josh Tenenbaum (Talk) Josh Tenenbaum
09:00 AM Talk 1: Zico Kolter - Differentiable Physics and Control (Talk) J. Zico Kolter
09:30 AM Talk 2: Emo Todorov - Physics-Based Control (Talk) Emanuel Todorov
10:00 AM Contributed Talk 1: ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics (Talk) Andrew E Spielberg
10:10 AM Contributed Talk 2: To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions (Talk) Tatiana López-Guevara
10:20 AM Contributed Talk 3: Learning Robotic Manipulation through Visual Planning and Acting (Talk) Angelina Wang
10:30 AM Coffee Break 1 (Posters) (Break)
Ananya Kumar, Siyu Huang, Huazhe Xu, Michael Janner, Parth Chadha, Nils Thuerey, Peter Y Lu, Maria Bauza, Anthony Tompkins, Guanya Shi, Thomas Baumeister, André Ofner, Zhi-Qi Cheng, Yuping Luo, Deepika Bablani, Jeroen Vanbaar, Kartic Subr, Tatiana López-Guevara, Devesh Jha, Fabian Fuchs, Stefano Rosa, Alison Pouplin, Alex Ray, Qi Liu, Eric Crawford
11:00 AM Talk 3: Jitendra Malik - Linking Perception and Action (Talk) Jitendra Malik
11:30 AM Talk 4: Chelsea Finn - An agent that can do many things 
(by modeling the world) (Talk) Chelsea Finn
12:00 PM Lunch Break (Break)
02:00 PM Talk 5: Peter Battaglia - Structure in Physical Intelligence (Talk) Peter Battaglia
02:30 PM Talk 6: Dan Yamins - The Objects of Our Curiosity: Intrinsic Motivation, Intuitive Physics and Self-Supervised Learning (Talk) Daniel Yamins
03:00 PM Coffee Break 2 (Posters) (Break)
03:30 PM Talk 7: Jeannette Bohg - On perceptual representations and how they interact with actions and physical models (Talk) Christin Jeannette Bohg
04:00 PM Talk 8: Leslie Kaelbling - Learning models of very large hybrid domains (Talk) Leslie Kaelbling
04:30 PM Talk 9: Marc Toussaint - Models & Abstractions for Physical Reasoning (Talk) Marc Toussaint
05:00 PM Panel Discussion <span> <a href="#"></a> </span>

Author Information

Jiajun Wu (MIT)

Jiajun Wu is a fifth-year Ph.D. student at Massachusetts Institute of Technology, advised by Professor Bill Freeman and Professor Josh Tenenbaum. His research interests lie on the intersection of computer vision, machine learning, and computational cognitive science. Before coming to MIT, he received his B.Eng. from Tsinghua University, China, advised by Professor Zhuowen Tu. He has also spent time working at research labs of Microsoft, Facebook, and Baidu.

Kelsey Allen (MIT)
Kevin Smith (MIT)
Jessica Hamrick (DeepMind)
Emmanuel Dupoux (Ecole des Hautes Etudes en Sciences Sociales)
Marc Toussaint (Universty Stuttgart)
Josh Tenenbaum (MIT)

Josh Tenenbaum is an Associate Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 1999, and was an Assistant Professor at Stanford University from 1999 to 2002. He studies learning and inference in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He focuses on problems of inductive generalization from limited data -- learning concepts and word meanings, inferring causal relations or goals -- and learning abstract knowledge that supports these inductive leaps in the form of probabilistic generative models or 'intuitive theories'. He has also developed several novel machine learning methods inspired by human learning and perception, most notably Isomap, an approach to unsupervised learning of nonlinear manifolds in high-dimensional data. He has been Associate Editor for the journal Cognitive Science, has been active on program committees for the CogSci and NIPS conferences, and has co-organized a number of workshops, tutorials and summer schools in human and machine learning. Several of his papers have received outstanding paper awards or best student paper awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, and Cognitive Science conferences. He is the recipient of the New Investigator Award from the Society for Mathematical Psychology (2005), the Early Investigator Award from the Society of Experimental Psychologists (2007), and the Distinguished Scientific Award for Early Career Contribution to Psychology (in the area of cognition and human learning) from the American Psychological Association (2008).

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