NeRFと制御バリア関数による視覚ベース制御の安全保証
Enforcing safety for vision-based controllers via Control Barrier Functions and Neural Radiance Fields
NeRFを用いて未知視点の画像を予測し、制御バリア関数に基づく安全フィルタを構築することで、視覚フィードバック制御の安全性を保証する手法を提案した。
著者: Mukun Tong, Charles Dawson, Chuchu Fan
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
To navigate complex environments, robots must increasingly use high-dimensional visual feedback (e.g. images) for control. However, relying on high-dimensional image data to make control decisions raises important questions; particularly, how might we prove the safety of a visual-feedback controller? Control barrier functions (CBFs) are powerful tools for certifying the safety of feedback controllers in the state-feedback setting, but CBFs have traditionally been poorly-suited to visual feedback control due to the need to predict future observations in order to evaluate the barrier function. In this work, we solve this issue by leveraging recent advances in neural radiance fields (NeRFs), which learn implicit representations of 3D scenes and can render images from previously-unseen camera perspectives, to provide single-step visual foresight for a CBF-based controller. This novel combination is able to filter out unsafe actions and intervene to preserve safety. We demonstrate the effect of our controller in real-time simulation experiments where it successfully prevents the robot from taking dangerous actions.
