Topic
Physics engine
About: Physics engine is a research topic. Over the lifetime, 491 publications have been published within this topic receiving 13886 citations.
Papers published on a yearly basis
Papers
More filters
••
24 Dec 2012TL;DR: A new physics engine tailored to model-based control, based on the modern velocity-stepping approach which avoids the difficulties with spring-dampers, which can compute both forward and inverse dynamics.
Abstract: We describe a new physics engine tailored to model-based control. Multi-joint dynamics are represented in generalized coordinates and computed via recursive algorithms. Contact responses are computed via efficient new algorithms we have developed, based on the modern velocity-stepping approach which avoids the difficulties with spring-dampers. Models are specified using either a high-level C++ API or an intuitive XML file format. A built-in compiler transforms the user model into an optimized data structure used for runtime computation. The engine can compute both forward and inverse dynamics. The latter are well-defined even in the presence of contacts and equality constraints. The model can include tendon wrapping as well as actuator activation states (e.g. pneumatic cylinders or muscles). To facilitate optimal control applications and in particular sampling and finite differencing, the dynamics can be evaluated for different states and controls in parallel. Around 400,000 dynamics evaluations per second are possible on a 12-core machine, for a 3D homanoid with 18 dofs and 6 active contacts. We have already used the engine in a number of control applications. It will soon be made publicly available.
4,018 citations
•
05 Dec 2016TL;DR: The interaction network is introduced, a model which can reason about how objects in complex systems interact, supporting dynamical predictions, as well as inferences about the abstract properties of the system, and is implemented using deep neural networks.
Abstract: Reasoning about objects, relations, and physics is central to human intelligence, and a key goal of artificial intelligence. Here we introduce the interaction network, a model which can reason about how objects in complex systems interact, supporting dynamical predictions, as well as inferences about the abstract properties of the system. Our model takes graphs as input, performs object- and relation-centric reasoning in a way that is analogous to a simulation, and is implemented using deep neural networks. We evaluate its ability to reason about several challenging physical domains: n-body problems, rigid-body collision, and non-rigid dynamics. Our results show it can be trained to accurately simulate the physical trajectories of dozens of objects over thousands of time steps, estimate abstract quantities such as energy, and generalize automatically to systems with different numbers and configurations of objects and relations. Our interaction network implementation is the first general-purpose, learnable physics engine, and a powerful general framework for reasoning about object and relations in a wide variety of complex real-world domains.
1,060 citations
•
TL;DR: A new simulator built on Unreal Engine that offers physically and visually realistic simulations for autonomous vehicles in real world and that is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols.
Abstract: Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments. We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals. Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (e.g. MavLink). The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols. In addition, the modular design enables various components to be easily usable independently in other projects. We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flights.
979 citations
••
15 May 2017TL;DR: In this paper, the authors present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for autonomous vehicles in real-world environments, including a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (e.g., MavLink).
Abstract: Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (eg MavLink) The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols In addition, the modular design enables various components to be easily usable independently in other projects We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flights
938 citations
••
01 Jun 1988TL;DR: This paper discusses collision detection and response in general, presents two collision detection algorithms, describes modeling collisions of arbitrary bodies using springs, and presents an analytical collision response algorithm for articulated rigid bodies that conserves linear and angular momentum.
Abstract: When several objects are moved about by computer animation, there is the chance that they will interpenetrate. This is often an undesired state, particularly if the animation is seeking to model a realistic world. Two issues are involved: detecting that a collision has occurred, and responding to it. The former is fundamentally a kinematic problem, involving the positional relationship of objects in the world. The latter is a dynamic problem, in that it involves predicting behavior according to physical laws. This paper discusses collision detection and response in general, presents two collision detection algorithms, describes modeling collisions of arbitrary bodies using springs, and presents an analytical collision response algorithm for articulated rigid bodies that conserves linear and angular momentum.
672 citations