Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images

In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term motion as response to those forces. Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging. We define intermediate physical abstractions called Newtonian scenarios and introduce Newtonian Neural Network ($N^3$) that learns to map a single image to a state in a Newtonian scenario. Our experimental evaluations show that our method can reliably predict dynamics of a query object from a single image. In addition, our approach can provide physical reasoning that supports the predicted dynamics in terms of velocity and force vectors. To spur research in this direction we compiled Visual Newtonian Dynamics (VIND) dataset that includes 6806 videos aligned with Newtonian scenarios represented using game engines, and 4516 still images with their ground truth dynamics

Analyzing helicopter evasive maneuver effectiveness against rocket-propelled grenades

It has long been acknowledged that military helicopters are vulnerable to ground-launched threats, in particular, the RPG-7 rocket-propelled grenade. Current helicopter threat mitigation strategies rely on a combination of operational tactics and selectively placed armor plating, which can help to mitigate but not entirely remove the threat. However, in recent years, a number of active protection systems designed to protect land-based vehicles from rocket and missile fire have been developed. These systems all use a sensor suite to detect, track, and predict the threat trajectory, which is then employed in the computation of an intercept trajectory for a defensive kill mechanism. Although a complete active protection system in its current form is unsuitable for helicopters, in this paper, it is assumed that the active protection system’s track and threat trajectory prediction subsystem could be used offline as a tool to develop tactics and techniques to counter the threat from rocket-propelled grenade attacks. It is further proposed that such a maneuver can be found by solving a pursuit–evasion differential game. Because the first stage in solving this problem is developing the capability to evaluate the game, nonlinear dynamic and spatial models for a helicopter, RPG-7 round, and gunner, and evasion strategies were developed and integrated into a new simulation engine. Analysis of the results from representative vignettes demonstrates that the simulation yields the value of the engagement pursuit–evasion game. It is also shown that, in the majority of cases, survivability can be significantly improved by performing an appropriate evasive maneuver. Consequently, this simulation may be used as an important tool for both designing and evaluating evasive tactics and is the first step in designing a maneuver-based active protection system, leading to improved rotorcraft survivability

On Testing the Simulation Theory

Can the theory that reality is a simulation be tested? We investigate this question based on the assumption that if the system performing the simulation is nite (i.e. has limited resources), then to achieve low computational complexity, such a system would, as in a video game, render content (reality) only at the moment that information becomes available for observation by a player and not at the moment of detection by a machine (that would be part of the simulation and whose detection would also be part of the internal computation performed by the Virtual Reality server before rendering content to the player). Guided by this principle we describe conceptual wave/particle duality experiments aimed at testing the simulation theory

A Framework for Designing 3d Virtual Environments

The process of design and development of virtual environments can be supported by tools and frameworks, to save time in technical aspects and focusing on the content. In this paper we present an academic framework which provides several levels of abstraction to ease this work. It includes state-of-the-art components we devised or integrated adopting open-source solutions in order to face specific problems. Its architecture is modular and customizable, the code is open-source.\u

Generating Levels That Teach Mechanics

The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.Comment: 8 pages, 7 figures, PCG Workshop at FDG 2018, 9th International Workshop on Procedural Content Generation (PCG2018

Parallelized Rigid Body Dynamics

Physics engines are collections of API-like software designed for video games, movies and scientific simulations. While physics engines often come in many shapes and designs, all engines can benefit from an increase in speed via parallelization. However, despite this need for increased speed, it is uncommon to encounter a parallelized physics engine today. Many engines are long-standing projects and changing them to support parallelization is too costly to consider as a practical matter. Parallelization needs to be considered from the design stages through completion to ensure adequate implementation. In this project we develop a realistic approach to simulate physics in a parallel environment. Utilizing many techniques we establish a practical approach to significantly reduce the run-time on a standard physics engine