A Compositional Object-Based Approach to Learning Physical Dynamics — mentioned by mbchang

Tue, 07 Mar 2017 07:04:30 GMT

Authors: Michael Chang, Tomer Ullman, Antonio Torralba, Joshua Tenenbaum Abstract: We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene configurations. We propose a factorization of a physical scene into composable object-based representations and a neural network architecture whose compositional structure factorizes object dynamics into pairwise interactions. Like a symbolic physics engine, the NPE is endowed with generic notions of objects and their interactions…

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A Compositional Object-Based Approach to Learning Physical Dynamics — mentioned by mbchang