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arXiv · 2301.11520

SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning

Dongseok Shim·Seungjae Lee·H. Jin Kim
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Abstract

As previous representations for reinforcement learning cannot effectively incorporate a human-intuitive understanding of the 3D environment, they usually suffer from sub-optimal performances. In this paper, we present Semantic-aware Neural Radiance Fields for Reinforcement Learning (SNeRL), which jointly optimizes semantic-aware neural radiance fields (NeRF) with a convolutional encoder to learn 3D-aware neural implicit representation from multi-view images. We introduce 3D semantic and distilled feature fields in parallel to the RGB radiance fields in NeRF to learn semantic and object-centric representation for reinforcement learning. SNeRL outperforms not only previous pixel-based representations but also recent 3D-aware representations both in model-free and model-based reinforcement learning.

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BibTeXRIS

Dongseok Shim, Seungjae Lee, H. Jin Kim. 2023-01-27. SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning. https://arxiv.org/abs/2301.11520

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