Visual Interaction Networks (1706.01433v1)

Published 5 Jun 2017 in cs.CV

Abstract: From just a glance, humans can make rich predictions about the future state of a wide range of physical systems. On the other hand, modern approaches from engineering, robotics, and graphics are often restricted to narrow domains and require direct measurements of the underlying states. We introduce the Visual Interaction Network, a general-purpose model for learning the dynamics of a physical system from raw visual observations. Our model consists of a perceptual front-end based on convolutional neural networks and a dynamics predictor based on interaction networks. Through joint training, the perceptual front-end learns to parse a dynamic visual scene into a set of factored latent object representations. The dynamics predictor learns to roll these states forward in time by computing their interactions and dynamics, producing a predicted physical trajectory of arbitrary length. We found that from just six input video frames the Visual Interaction Network can generate accurate future trajectories of hundreds of time steps on a wide range of physical systems. Our model can also be applied to scenes with invisible objects, inferring their future states from their effects on the visible objects, and can implicitly infer the unknown mass of objects. Our results demonstrate that the perceptual module and the object-based dynamics predictor module can induce factored latent representations that support accurate dynamical predictions. This work opens new opportunities for model-based decision-making and planning from raw sensory observations in complex physical environments.

Authors (6)

Nicholas Watters (7 papers)
Andrea Tacchetti (26 papers)
Razvan Pascanu (138 papers)
Peter Battaglia (40 papers)
Daniel Zoran (25 papers)
Theophane Weber (23 papers)

Citations (271)

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Summary

Visual Interaction Networks: A Survey

The paper "Visual Interaction Networks" introduces a novel, general-purpose model known as the Visual Interaction Network (VIN) designed for predicting the future states of physical systems from sequences of visual observations. This model bridges a significant gap in artificial intelligence by enhancing the prediction of physical interactions and future states using raw sensory data, a challenge previously unmet by existing systems lacking the flexibility and generalization capability of human cognition.

Overview and Contributions

The VIN architecture leverages both convolutional and recurrent neural networks to decompose a visual scene into factored, latent object representations, subsequently progressing these states to predict future dynamics. This is achieved through a perceptual front-end utilizing convolutional neural networks (CNNs) for visual encoding and a dynamics predictor built on interaction networks (INs) for understanding and predicting interactions among objects. The paper demonstrates the model's ability to anticipate accurate trajectories over several hundred time steps from just six input video frames, showcasing its versatility in diverse physical settings including systems with invisible entities and varying object masses.

Key Results and Discussions

The efficacy of the VIN is illustrated across multiple simulated systems with varying interactions, such as spring, gravity, and magnetic billiards, which pose different levels of dynamic complexity and require robust prediction capabilities. Strong numerical results emphasize VIN's capacity to outperform other baselines, including Visual RNNs and Visual LSTMs, especially in predicting state sequences over extended periods.

VIN's design highlights an intrinsic advantage—its resilience against noise introduced by the visual encoder, a trait not shared by state-to-state models that rely on noiseless input. This characteristic endows VIN with superior generalization during long rollouts, surpassing even state-to-state architectures numerically and qualitatively in prolonged interaction scenarios.

Implications and Future Directions

The implications of this research primarily rest on improving model-based decision-making and planning in artificial intelligence. The integration of perceptual models with latent object and dynamic representations opens pathways for enhancing autonomous agent capabilities, potentially influencing fields ranging from robotics to virtual reality simulations where understanding physical dynamics directly from images is crucial.

In future work, the exploration of VINs can extend towards more complex and realistic environments involving multi-faceted interactions and occlusions. Furthermore, the understanding of how noise in visual inputs benefits dynamic prediction offers a valuable direction for designing robust AI systems that can learn and anticipate under uncertainty—akin to biological beings. Continuous improvements in learning spatial-temporal patterns and expanding the architecture's scope to manage real-world scenarios remain critical for advancing AI applications towards more human-like cognitive reasoning.

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