動力学拡張ニューラルオブジェクトの微分可能物理シミュレーション
Differentiable Physics Simulation of Dynamics-Augmented Neural Objects
NeRFなどの密度場で表現された物体の質量・慣性・摩擦を推定し、微分可能接触モデルでシミュレーションする手法を提案。実動画から石鹸の摩擦係数を学習し、ロボットアームによる操作軌道の最適化を実現した。
著者: Simon Le Cleac'h, Hong-Xing Yu, Michelle Guo, Taylor A. Howell, Ruohan Gao, Jiajun Wu, Zachary Manchester, Mac Schwager
分類: cs.RO, cs.AI
原文アブストラクト
We present a differentiable pipeline for simulating the motion of objects that represent their geometry as a continuous density field parameterized as a deep network. This includes Neural Radiance Fields (NeRFs), and other related models. From the density field, we estimate the dynamical properties of the object, including its mass, center of mass, and inertia matrix. We then introduce a differentiable contact model based on the density field for computing normal and friction forces resulting from collisions. This allows a robot to autonomously build object models that are visually and \emph{dynamically} accurate from still images and videos of objects in motion. The resulting Dynamics-Augmented Neural Objects (DANOs) are simulated with an existing differentiable simulation engine, Dojo, interacting with other standard simulation objects, such as spheres, planes, and robots specified as URDFs. A robot can use this simulation to optimize grasps and manipulation trajectories of neural objects, or to improve the neural object models through gradient-based real-to-simulation transfer. We demonstrate the pipeline to learn the coefficient of friction of a bar of soap from a real video of the soap sliding on a table. We also learn the coefficient of friction and mass of a Stanford bunny through interactions with a Panda robot arm from synthetic data, and we optimize trajectories in simulation for the Panda arm to push the bunny to a goal location.
