Browsing by Author "Andrews, Sheldon"

Now showing 1 - 6 of 6
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    Coupling Friction with Visual Appearance
    (ACM, 2021) Andrews, Sheldon; Nassif, Loic; Erleben, Kenny; Kry, Paul G.; Narain, Rahul and Neff, Michael and Zordan, Victor
    We present a novel meso-scale model for computing anisotropic and asymmetric friction for contacts in rigid body simulations that is based on surface facet orientations. The main idea behind our approach is to compute a direction dependent friction coefficient that is determined by an object's roughness. Specifically, where the friction is dependent on asperity interlocking, but at a scale where surface roughness is also a visual characteristic of the surface. A GPU rendering pipeline is employed to rasterize surfaces using a shallow depth orthographic projection at each contact point in order to sample facet normal information from both surfaces, which we then combine to produce direction dependent friction coefficients that can be directly used in typical LCP contact solvers, such as the projected Gauss-Seidel method. We demonstrate our approach with a variety of rough textures, where the roughness is both visible in the rendering and in the motion produced by the physical simulation.
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    Distant Collision Response in Rigid Body Simulations
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Coevoet, Eulalie; Andrews, Sheldon; Relles, Denali; Kry, Paul G.; Bender, Jan and Popa, Tiberiu
    We use a finite element model to predict the vibration response of objects in a rigid body simulation, such that rigid objects are augmented to provide a plausible elastic collision response between distant objects due to vibration. We start with a generalized eigenvalue decomposition of the elastic model to precompute a response to an impact at any point on an elastic object with fixed boundary conditions. Then, given a collision between objects, we generate an approximate response impulse to distribute to other objects already in contact with the colliding bodies. This can lead to distant impacts causing an object to slip, or a delicate stack of objects to fall. We also use a geodesic distance based spatial attenuation approximation for travelling waves in objects to respond to an impact at one contact with an impulse at other locations. This response ultimately allows a long distance relationship between contacts, both across a single object being struck, but also traversing the contact graph of a larger collection of objects. We qualitatively validate our approach with a ground truth simulation, and demonstrate a number of scenarios where a long distance relationship between contacts is valuable.
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    Graph Partitioning Algorithms for Rigid Body Simulations
    (The Eurographics Association, 2022) Liu, Yinchu; Andrews, Sheldon; Pelechano, Nuria; Vanderhaeghe, David
    We propose several graph partitioning algorithms for improving the performance of rigid body simulations. The algorithms operate on the graph formed by rigid bodies (nodes) and constraints (edges), producing non-overlapping and contiguous sub-systems that can be simulated in parallel by a domain decomposition technique. We demonstrate that certain partitioning algorithms reduce the computational time of the solver, and graph refinement techniques that reduce coupling between sub-systems, such as the Kernighan-Lin and Fiduccia-Mattheyses algorithms, give additional performance improvements.
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    Synthesizing Get-Up Motions for Physics-based Characters
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Frezzato, Anthony; Tangri, Arsh; Andrews, Sheldon; Dominik L. Michels; Soeren Pirk
    We propose a method for synthesizing get-up motions for physics-based humanoid characters. Beginning from a supine or prone state, our objective is not to imitate individual motion clips, but to produce motions that match input curves describing the style of get-up motion. Our framework uses deep reinforcement learning to learn control policies for the physics-based character. A latent embedding of natural human poses is computed from a motion capture database, and the embedding is furthermore conditioned on the input features. We demonstrate that our approach can synthesize motions that follow the style of user authored curves, as well as curves extracted from reference motions. In the latter case, motions of the physics-based character resemble the original motion clips. New motions can be synthesized easily by changing only a small number of controllable parameters. We also demonstrate the success of our controllers on rough and inclined terrain.
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    Too Stiff, Too Strong, Too Smart: Evaluating Fundamental Problems with Motion Control Policies
    (ACM Association for Computing Machinery, 2023) Xie, Kaixiang; Xu, Pei; Andrews, Sheldon; Zordan, Victor B.; Kry, Paul G.; Wang, Huamin; Ye, Yuting; Victor Zordan
    Deep reinforcement learning (DRL) methods have demonstrated impressive results for skilled motion synthesis of physically based characters, and while these methods perform well in terms of tracking reference motions or achieving complex tasks, several concerns arise when evaluating the naturalness of the motion. In this paper, we conduct a preliminary study of specific quantitative metrics for measuring the naturalness of motion produced by DRL control policies beyond their visual appearance. Namely, we propose to study the stiffness of the control policy, in anticipation that it will influence how the character behaves in the presence of external perturbation. Second, we establish two baselines for strength that allow evaluating the use of joint torques in comparison to human performance. Third, we propose the study of variability to reveal the unnatural precision of control policies and how they compare to real human motion. In sum, we aim to establish repeatable measures to assess the naturalness of control policies produced by DRL methods, and we present a set of comparisons from state-of-the-art systems. Finally, we propose simple modifications to improve realism on these axes.
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    VRIPHYS 2018: Frontmatter
    (Eurographics Association, 2018) Andrews, Sheldon; Erleben, Kenny; Jaillet, Fabrice; Zachmann, Gabriel; Andrews, Sheldon and Erleben, Kenny and Jaillet, Fabrice and Zachmann, Gabriel