微分可能ロボットレンダリング
Differentiable Robot Rendering
ロボットの見た目を制御パラメータで微分可能に描画する手法を提案し、画像からの姿勢復元や視覚言語モデルによる制御に応用した。
著者: Ruoshi Liu, Alper Canberk, Shuran Song, Carl Vondrick
分類: cs.RO, cs.CV, cs.GR
原文アブストラクト
Vision foundation models trained on massive amounts of visual data have shown unprecedented reasoning and planning skills in open-world settings. A key challenge in applying them to robotic tasks is the modality gap between visual data and action data. We introduce differentiable robot rendering, a method allowing the visual appearance of a robot body to be directly differentiable with respect to its control parameters. Our model integrates a kinematics-aware deformable model and Gaussians Splatting and is compatible with any robot form factors and degrees of freedom. We demonstrate its capability and usage in applications including reconstruction of robot poses from images and controlling robots through vision language models. Quantitative and qualitative results show that our differentiable rendering model provides effective gradients for robotic control directly from pixels, setting the foundation for the future applications of vision foundation models in robotics.
