1,595 research outputs found
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics
Physical simulators have been widely used in robot planning and control.
Among them, differentiable simulators are particularly favored, as they can be
incorporated into gradient-based optimization algorithms that are efficient in
solving inverse problems such as optimal control and motion planning.
Simulating deformable objects is, however, more challenging compared to rigid
body dynamics. The underlying physical laws of deformable objects are more
complex, and the resulting systems have orders of magnitude more degrees of
freedom and therefore they are significantly more computationally expensive to
simulate. Computing gradients with respect to physical design or controller
parameters is typically even more computationally challenging. In this paper,
we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical
simulator for deformable objects, ChainQueen, based on the Moving Least Squares
Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects
including contact and can be seamlessly incorporated into inference, control
and co-design systems. We demonstrate that our simulator achieves high
precision in both forward simulation and backward gradient computation. We have
successfully employed it in a diverse set of control tasks for soft robots,
including problems with nearly 3,000 decision variables.Comment: In submission to ICRA 2019. Supplemental Video:
https://www.youtube.com/watch?v=4IWD4iGIsB4 Project Page:
https://github.com/yuanming-hu/ChainQuee
Anelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversion
We introduce a technique to compute exact anelastic sensitivity kernels in
the time domain using parsimonious disk storage. The method is based on a
reordering of the time loop of time-domain forward/adjoint wave propagation
solvers combined with the use of a memory buffer. It avoids instabilities that
occur when time-reversing dissipative wave propagation simulations. The total
number of required time steps is unchanged compared to usual acoustic or
elastic approaches. The cost is reduced by a factor of 4/3 compared to the case
in which anelasticity is partially accounted for by accommodating the effects
of physical dispersion. We validate our technique by performing a test in which
we compare the sensitivity kernel to the exact kernel obtained by
saving the entire forward calculation. This benchmark confirms that our
approach is also exact. We illustrate the importance of including full
attenuation in the calculation of sensitivity kernels by showing significant
differences with physical-dispersion-only kernels
Fast GPU-Based Two-Way Continuous Collision Handling
Step-and-project is a popular way to simulate non-penetrated deformable
bodies in physically-based animation. First integrating the system in time
regardless of contacts and post resolving potential intersections practically
strike a good balance between plausibility and efficiency. However, existing
methods could be defective and unsafe when the time step is large, taking risks
of failures or demands of repetitive collision testing and resolving that
severely degrade performance. In this paper, we propose a novel two-way method
for fast and reliable continuous collision handling. Our method launches the
optimization at both ends of the intermediate time-integrated state and the
previous intersection-free state, progressively generating a piecewise-linear
path and finally reaching a feasible solution for the next time step.
Technically, our method interleaves between a forward step and a backward step
at a low cost, until the result is conditionally converged. Due to a set of
unified volume-based contact constraints, our method can flexibly and reliably
handle a variety of codimensional deformable bodies, including volumetric
bodies, cloth, hair and sand. The experiments show that our method is safe,
robust, physically faithful and numerically efficient, especially suitable for
large deformations or large time steps
DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets
Robotic grasping of 3D deformable objects is critical for real-world
applications such as food handling and robotic surgery. Unlike rigid and
articulated objects, 3D deformable objects have infinite degrees of freedom.
Fully defining their state requires 3D deformation and stress fields, which are
exceptionally difficult to analytically compute or experimentally measure.
Thus, evaluating grasp candidates for grasp planning typically requires
accurate, but slow 3D finite element method (FEM) simulation. Sampling-based
grasp planning is often impractical, as it requires evaluation of a large
number of grasp candidates. Gradient-based grasp planning can be more
efficient, but requires a differentiable model to synthesize optimal grasps
from initial candidates. Differentiable FEM simulators may fill this role, but
are typically no faster than standard FEM. In this work, we propose learning a
predictive graph neural network (GNN), DefGraspNets, to act as our
differentiable model. We train DefGraspNets to predict 3D stress and
deformation fields based on FEM-based grasp simulations. DefGraspNets not only
runs up to 1500 times faster than the FEM simulator, but also enables fast
gradient-based grasp optimization over 3D stress and deformation metrics. We
design DefGraspNets to align with real-world grasp planning practices and
demonstrate generalization across multiple test sets, including real-world
experiments.Comment: To be published in the IEEE Conference on Robotics and Automation
(ICRA), 202
Rieoptax: Riemannian Optimization in JAX
We present Rieoptax, an open source Python library for Riemannian
optimization in JAX. We show that many differential geometric primitives, such
as Riemannian exponential and logarithm maps, are usually faster in Rieoptax
than existing frameworks in Python, both on CPU and GPU. We support various
range of basic and advanced stochastic optimization solvers like Riemannian
stochastic gradient, stochastic variance reduction, and adaptive gradient
methods. A distinguishing feature of the proposed toolbox is that we also
support differentially private optimization on Riemannian manifolds
Simulation of hyperelastic materials in real-time using Deep Learning
The finite element method (FEM) is among the most commonly used numerical
methods for solving engineering problems. Due to its computational cost,
various ideas have been introduced to reduce computation times, such as domain
decomposition, parallel computing, adaptive meshing, and model order reduction.
In this paper we present U-Mesh: a data-driven method based on a U-Net
architecture that approximates the non-linear relation between a contact force
and the displacement field computed by a FEM algorithm. We show that deep
learning, one of the latest machine learning methods based on artificial neural
networks, can enhance computational mechanics through its ability to encode
highly non-linear models in a compact form. Our method is applied to two
benchmark examples: a cantilever beam and an L-shape subject to moving punctual
loads. A comparison between our method and proper orthogonal decomposition
(POD) is done through the paper. The results show that U-Mesh can perform very
fast simulations on various geometries, mesh resolutions and number of input
forces with very small errors
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