強化学習のためのクエリベース意味論的ガウシアンフィールドによるシーン表現
Query-based Semantic Gaussian Field for Scene Representation in Reinforcement Learning
3Dガウシアンスプラッティングと階層的意味論エンコーディングを組み合わせ、強化学習エージェントのための効率的で意味情報を保持したシーン表現を学習する手法を提案。
著者: Jiaxu Wang, Ziyi Zhang, Qiang Zhang, Jia Li, Jingkai Sun, Mingyuan Sun, Junhao He, Renjing Xu
分類: cs.RO
原文アブストラクト
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.
