NeRFを教師信号に用いた強化学習
Reinforcement Learning with Neural Radiance Fields
Neural Radiance Fieldsを監督信号として画像から潜在状態表現を学習し、ロボット物体操作タスクの強化学習性能を向上させる手法NeRF-RLを提案。
著者: Danny Driess, Ingmar Schubert, Pete Florence, Yunzhu Li, Marc Toussaint
分類: cs.LG, cs.CV, cs.RO
原文アブストラクト
It is a long-standing problem to find effective representations for training reinforcement learning (RL) agents. This paper demonstrates that learning state representations with supervision from Neural Radiance Fields (NeRFs) can improve the performance of RL compared to other learned representations or even low-dimensional, hand-engineered state information. Specifically, we propose to train an encoder that maps multiple image observations to a latent space describing the objects in the scene. The decoder built from a latent-conditioned NeRF serves as the supervision signal to learn the latent space. An RL algorithm then operates on the learned latent space as its state representation. We call this NeRF-RL. Our experiments indicate that NeRF as supervision leads to a latent space better suited for the downstream RL tasks involving robotic object manipulations like hanging mugs on hooks, pushing objects, or opening doors. Video: https://dannydriess.github.io/nerf-rl
