publications

2024

1. Differentiable solver for time-dependent deformation problems with contact

   Zizhou Huang, Davi Colli Tozoni, Arvi Gjoka , and 4 more authors

   ACM Trans. Graph., May 2024

   Abs

   We introduce a general differentiable solver for time-dependent deformation problems with contact and friction. Our approach uses a finite element discretization with a high-order time integrator coupled with the recently proposed incremental potential contact method for handling contact and friction forces to solve ODE- and PDE-constrained optimization problems on scenes with complex geometry. It supports static and dynamic problems and differentiation with respect to all physical parameters involved in the physical problem description, which include shape, material parameters, friction parameters, and initial conditions. Our analytically derived adjoint formulation is efficient, with a small overhead (typically less than 10% for nonlinear problems) over the forward simulation, and shares many similarities with the forward problem, allowing the reuse of large parts of existing forward simulator code.We implement our approach on top of the open-source PolyFEM library and demonstrate the applicability of our solver to shape design, initial condition optimization, and material estimation on both simulated results and physical validations.

2. Soft Pneumatic Actuator Design using Differentiable Simulation

   Arvi Gjoka, Espen Knoop, Moritz Bächer , and 2 more authors

   In Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24 , Denver, CO, USA, May 2024

   Abs

   We propose a computational design pipeline for pneumatically-actuated soft robots interacting with their environment through contact. We optimize the shape of the robot with a shape optimization approach, using a physically-accurate high-order finite element model for the forward simulation. Our approach enables fine-grained control over both deformation and contact forces by optimizing the shape of internal cavities, which we exploit to design pneumatically-actuated robots that can assume user-prescribed poses, or apply user-controlled forces. We demonstrate the efficacy of our method on two artistic and two functional examples.

2021

1. An Extensible Benchmark Suite for Learning to Simulate Physical Systems

   Karl Otness, Arvi Gjoka, Joan Bruna , and 4 more authors

   In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1) , May 2021

2020

1. The iWildCam 2020 Competition Dataset

   Sara Beery, Elijah Cole, and Arvi Gjoka

   May 2020

2019

1. HistoryTracker: Minimizing Human Interactions in Baseball Game Annotation

   Jorge Piazentin Ono, Arvi Gjoka, Justin Salamon , and 2 more authors

   In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems , Glasgow, Scotland Uk, May 2019

   Abs

   The sport data tracking systems available today are based on specialized hardware (high-definition cameras, speed radars, RFID) to detect and track targets on the field. While effective, implementing and maintaining these systems pose a number of challenges, including high cost and need for close human monitoring. On the other hand, the sports analytics community has been exploring human computation and crowdsourcing in order to produce tracking data that is trustworthy, cheaper and more accessible. However, state-of-the-art methods require a large number of users to perform the annotation, or put too much burden into a single user. We propose HistoryTracker, a methodology that facilitates the creation of tracking data for baseball games by warm-starting the annotation process using a vast collection of historical data. We show that HistoryTracker helps users to produce tracking data in a fast and reliable way.