Related papers: Predicting Physical Object Properties from Video

Predicting Physical Object Properties from Video

URL: http://arxiv.org/abs/2206.00930v1
Date: Thu, 2 Jun 2022 08:46:22 GMT
Title: Predicting Physical Object Properties from Video
Authors: Martin Link, Max Schwarz, Sven Behnke
Abstract summary: We present a novel approach to estimating physical properties of objects from video. Our approach consists of a physics engine and a correction estimator. We demonstrate faster and more robust convergence of the learned method in several simulated 2D scenarios.
Score: 28.19031441659854
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We present a novel approach to estimating physical properties of objects from video. Our approach consists of a physics engine and a correction estimator. Starting from the initial observed state, object behavior is simulated forward in time. Based on the simulated and observed behavior, the correction estimator then determines refined physical parameters for each object. The method can be iterated for increased precision. Our approach is generic, as it allows for the use of an arbitrary - not necessarily differentiable - physics engine and correction estimator. For the latter, we evaluate both gradient-free hyperparameter optimization and a deep convolutional neural network. We demonstrate faster and more robust convergence of the learned method in several simulated 2D scenarios focusing on bin situations.

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