7 papers
DUPLEX: Agentic Dual-System Planning via LLM-Driven Information Extraction
Keru Hua, Ding Wang, Yaoying Gu +1
While Large Language Models (LLMs) provide semantic flexibility for robotic task planning, their susceptibility to hallucination and logical inconsistency limits their reliability…
Target-Bench: Can Video World Models Achieve Mapless Path Planning with Semantic Targets?
Dingrui Wang, Zhihao Liang, Hongyuan Ye +13
While recent video world models can generate highly realistic videos, their ability to perform semantic reasoning and planning remains unclear and unquantified. We introduce Target…
Enhancing Physical Consistency in Lightweight World Models
Dingrui Wang, Zhexiao Sun, Zhouheng Li +8
A major challenge in deploying world models is the trade-off between size and performance. Large world models can capture rich physical dynamics but require massive computing resou…
A Survey: Learning Embodied Intelligence from Physical Simulators and World Models
Xiaoxiao Long, Qingrui Zhao, Kaiwen Zhang +15
The pursuit of artificial general intelligence (AGI) has placed embodied intelligence at the forefront of robotics research. Embodied intelligence focuses on agents capable of perc…
Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Yuan Gao, Mattia Piccinini, Yuchen Zhang +12
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing hav…
DualAD: Dual-Layer Planning for Reasoning in Autonomous Driving
Dingrui Wang, Marc Kaufeld, Johannes Betz
We present a novel autonomous driving framework, DualAD, designed to imitate human reasoning during driving. DualAD comprises two layers: a rule-based motion planner at the bottom…