We demonstrate model-based, visual robot manipulation of deformable linear objects. Our
approach is based on a state-space representation of the physical system that the robot …

This review article explores the foundation of laypeople's understanding of the physical
world rooted in perceptual experience. Beginning with a concise historical overview of the …

When humans observe a physical system, they can easily locate objects, understand their
interactions, and anticipate future behavior, even in settings with complicated and previously …

The ability to perform causal and counterfactual reasoning are central properties of human
intelligence. Decision-making systems that can perform these types of reasoning have the …

Learning robot tasks or controllers using deep reinforcement learning has been proven
effective in simulations. Learning in simulation has several advantages. For example, one …

We propose a model that is able to perform unsupervised physical parameter estimation of
systems from video, where the differential equations governing the scene dynamics are …

To make progress in science, we often build abstract representations of physical systems
that meaningfully encode information about the systems. Such representations ignore …

Distilling interpretable physical laws from videos has led to expanded interest in the
computer vision community recently thanks to the advances in deep learning, but still …

We aim to perform unsupervised discovery of objects and their states such as location and
velocity, as well as physical system parameters such as mass and gravity from video–given …

While the basic laws of Newtonian mechanics are well understood, explaining a physical
scenario still requires manually modeling the problem with suitable equations and …