Fast and Feature-Complete Differentiable Physics for Articulated Rigid
Bodies with Contact
- URL: http://arxiv.org/abs/2103.16021v2
- Date: Thu, 1 Apr 2021 16:59:11 GMT
- Title: Fast and Feature-Complete Differentiable Physics for Articulated Rigid
Bodies with Contact
- Authors: Keenon Werling, Dalton Omens, Jeongseok Lee, Ioannis Exarchos, C.
Karen Liu
- Abstract summary: We present a differentiable physics engine that supports Lagrangian dynamics and hard contact constraints for articulated rigid body simulation.
Our differentiable physics engine offers a complete set of features that are typically only available in non-differentiable physics simulators.
- Score: 13.502749968646519
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a fast and feature-complete differentiable physics engine that
supports Lagrangian dynamics and hard contact constraints for articulated rigid
body simulation. Our differentiable physics engine offers a complete set of
features that are typically only available in non-differentiable physics
simulators commonly used by robotics applications. We solve contact constraints
precisely using linear complementarity problems (LCPs). We present efficient
and novel analytical gradients through the LCP formulation of inelastic contact
that exploit the sparsity of the LCP solution. We support complex contact
geometry, and gradients approximating continuous-time elastic collision. We
also introduce a novel method to compute complementarity-aware gradients that
help downstream optimization tasks avoid stalling in saddle points. We show
that an implementation of this combination in an existing physics engine (DART)
is capable of a 45x single-core speedup over finite-differencing in computing
analytical Jacobians for a single timestep, while preserving all the
expressiveness of original DART.
Related papers
- Jade: A Differentiable Physics Engine for Articulated Rigid Bodies with
Intersection-Free Frictional Contact [5.70896453969985]
Jade is a differentiable physics engine for articulated rigid bodies.
It offers features including intersection-free collision simulation and stable LCP solutions for multiple frictional contacts.
arXiv Detail & Related papers (2023-09-09T07:39:36Z) - NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with
Spatial-temporal Decomposition [67.46012350241969]
This paper proposes a general acceleration methodology called NeuralStagger.
It decomposing the original learning tasks into several coarser-resolution subtasks.
We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations.
arXiv Detail & Related papers (2023-02-20T19:36:52Z) - Fast Aquatic Swimmer Optimization with Differentiable Projective
Dynamics and Neural Network Hydrodynamic Models [23.480913364381664]
Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of interest to biologists and engineers.
We present a novel, fully differentiable hybrid approach to FSI that combines a 2D numerical simulation for the deformable solid structure of the swimmer.
We demonstrate the computational efficiency and differentiability of our hybrid simulator on a 2D carangiform swimmer.
arXiv Detail & Related papers (2022-03-30T15:21:44Z) - Physics Informed RNN-DCT Networks for Time-Dependent Partial
Differential Equations [62.81701992551728]
We present a physics-informed framework for solving time-dependent partial differential equations.
Our model utilizes discrete cosine transforms to encode spatial and recurrent neural networks.
We show experimental results on the Taylor-Green vortex solution to the Navier-Stokes equations.
arXiv Detail & Related papers (2022-02-24T20:46:52Z) - PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable
Physics [89.81550748680245]
We introduce a new differentiable physics benchmark called PasticineLab.
In each task, the agent uses manipulators to deform the plasticine into the desired configuration.
We evaluate several existing reinforcement learning (RL) methods and gradient-based methods on this benchmark.
arXiv Detail & Related papers (2021-04-07T17:59:23Z) - DiffPD: Differentiable Projective Dynamics with Contact [65.88720481593118]
We present DiffPD, an efficient differentiable soft-body simulator with implicit time integration.
We evaluate the performance of DiffPD and observe a speedup of 4-19 times compared to the standard Newton's method in various applications.
arXiv Detail & Related papers (2021-01-15T00:13:33Z) - Fast Gravitational Approach for Rigid Point Set Registration with
Ordinary Differential Equations [79.71184760864507]
This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA)
In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field.
We show that the new method class has characteristics not found in previous alignment methods.
arXiv Detail & Related papers (2020-09-28T15:05:39Z) - Scalable Differentiable Physics for Learning and Control [99.4302215142673]
Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments.
We develop a scalable framework for differentiable physics that can support a large number of objects and their interactions.
arXiv Detail & Related papers (2020-07-04T19:07:51Z) - ADD: Analytically Differentiable Dynamics for Multi-Body Systems with
Frictional Contact [26.408218913234872]
We present a differentiable dynamics solver that is able to handle frictional contact for rigid and deformable objects.
Through a principled mollification of normal and tangential contact forces, our method circumvents the main difficulties inherent to the non-smooth nature of frictional contact.
arXiv Detail & Related papers (2020-07-02T09:51:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.