Target-Bench: Can World Models Achieve Mapless Path Planning with Semantic Targets?
Authors: name: Dingrui Wang; name: Hongyuan Ye; name: Zhihao Liang; name: Zhexiao Sun; name: Zhaowei Lu; name: Yuchen Zhang; name: Yuyu Zhao; name: Yuan Gao; name: Marvin Seegert; name: Finn Schäfer; name: Haotong Qin; name: Wei Li; name: Luigi Palmieri; name: Felix Jahncke; name: Mattia Piccinini; name: Johannes Betz
Published: 2025-11-21 21:36:02

Abstract: While recent world models generate highly realistic videos, their ability to perform robot path planning remains unclear and unquantified. We introduce Target-Bench, the first benchmark specifically designed to evaluate world models on mapless path planning toward semantic targets in real-world environments. Target-Bench provides 450 robot-collected video sequences spanning 45 semantic categories with SLAM-based ground truth trajectories. Our evaluation pipeline recovers camera motion from generated videos and measures planning performance using five complementary metrics that quantify target-reaching capability, trajectory accuracy, and directional consistency. We evaluate state-of-the-art models including Sora 2, Veo 3.1, and the Wan series. The best off-the-shelf model (Wan2.2-Flash) achieves only 0.299 overall score, revealing significant limitations in current world models for robotic planning tasks. We show that fine-tuning an open-source 5B-parameter model on only 325 scenarios from our dataset achieves 0.345 overall score -- an improvement of more than 400% over its base version (0.066) and 15% higher than the best off-the-shelf model. We will open-source the code and dataset.