Query-based Semantic Gaussian Field for Scene Representation in Reinforcement Learning

TL;DR

This paper introduces a novel 3D scene representation method using Query-based Generalizable 3D Gaussian Splatting, improving semantic and geometric understanding for reinforcement learning tasks.

Contribution

It presents the first application of 3D Gaussian Splatting for scene representation in RL, with hierarchical semantic encoding for enhanced geometric and semantic detail.

Findings

Outperforms five baseline methods across 10 RL tasks

Achieves top success rates on 8 tasks, second-best on 2

Demonstrates improved scene understanding for RL agents

Abstract

Latent scene representation plays a significant role in training reinforcement learning (RL) agents. To obtain good latent vectors describing the scenes, recent works incorporate the 3D-aware latent-conditioned NeRF pipeline into scene representation learning. However, these NeRF-related methods struggle to perceive 3D structural information due to the inefficient dense sampling in volumetric rendering. Moreover, they lack fine-grained semantic information included in their scene representation vectors because they evenly consider free and occupied spaces. Both of them can destroy the performance of downstream RL tasks. To address the above challenges, we propose a novel framework that adopts the efficient 3D Gaussian Splatting (3DGS) to learn 3D scene representation for the first time. In brief, we present the Query-based Generalizable 3DGS to bridge the 3DGS technique and scene representations with more geometrical awareness than those in NeRFs. Moreover, we present the Hierarchical Semantics Encoding to ground the fine-grained semantic features to 3D Gaussians and further distilled to the scene representation vectors. We conduct extensive experiments on two RL platforms including Maniskill2 and Robomimic across 10 different tasks. The results show that our method outperforms the other 5 baselines by a large margin. We achieve the best success rates on 8 tasks and the second-best on the other two tasks.