NIPS 2016
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Intuitive Physics

Adam Lerer · Jiajun Wu · Josh Tenenbaum · Emmanuel Dupoux · Rob Fergus

Hilton Diag. Mar, Blrm. C

Despite recent progress, AI is still far away from achieving common sense reasoning. One area that is gathering a lot of interest is that of intuitive or naive physics. It concerns the ability that humans and, to a certain extent, infants and animals have to predict outcomes of physical interactions involving macroscopic objects. There is extensive experimental evidence that infants can predict the outcome of events based on physical concepts such as gravity, solidity, object permanence and conservation of shape and number, at an early stage of development, although there is also evidence that this capacity develops through time and experience. Recent work has attempted to build neural models that can make predictions about stability, collisions, forces and velocities from images or videos, or interactions with an environment. Such models could be both used to understand the cognitive and neural underpinning of naive physics in humans, but also to provide with AI applications more better inference and reasoning abilities.

This workshop will bring together researchers in machine learning, computer vision, robotics, computational neuroscience, and cognitive development to discuss artificial systems that capture or model intuitive physics by learning from footage of, or interactions with a real or simulated environment. 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:
- Learning models of Newtonian physics (deep networks, structured probabilistic generative models, physics engines)
- How to combine model-based and bottom-up approaches to intuitive physics
- Application of intuitive physics models to higher-level tasks such as navigation, video prediction, robotics, etc.
- How cognitive science and computational neuroscience literature may inform the design of artificial systems for physical prediction
- Methodology for comparing models of infant learning with clinical studies
- Development of new datasets or platforms for intuitive physics and visual commonsense

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Timezone: America/Los_Angeles


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