VL-TGS: 視覚言語モデルを用いたマップレス屋外環境における軌道生成と選択
VL-TGS: Trajectory Generation and Selection using Vision Language Models in Mapless Outdoor Environments
マップレス屋外環境で、走行可能性制約を満たし人間らしい経路を生成するため、CVAEと視覚言語モデルを組み合わせた軌道生成・選択手法を提案した。
著者: Daeun Song, Jing Liang, Xuesu Xiao, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present a multi-modal trajectory generation and selection algorithm for real-world mapless outdoor navigation in human-centered environments. Such environments contain rich features like crosswalks, grass, and curbs, which are easily interpretable by humans, but not by mobile robots. We aim to compute suitable trajectories that (1) satisfy the environment-specific traversability constraints and (2) generate human-like paths while navigating on crosswalks, sidewalks, etc. Our formulation uses a Conditional Variational Autoencoder (CVAE) generative model enhanced with traversability constraints to generate multiple candidate trajectories for global navigation. We develop a visual prompting approach and leverage the Visual Language Model's (VLM) zero-shot ability of semantic understanding and logical reasoning to choose the best trajectory given the contextual information about the task. We evaluate our method in various outdoor scenes with wheeled robots and compare the performance with other global navigation algorithms. In practice, we observe an average improvement of 20.81% in satisfying traversability constraints and 28.51% in terms of human-like navigation in four different outdoor navigation scenarios.
