The grid-centric perception is a crucial field for mobile robot perception and navigation.
Nonetheless, the grid-centric perception is less prevalent than object-centric perception as …

Understanding how the 3D scene evolves is vital for making decisions in autonomous
driving. Most existing methods achieve this by predicting the movements of object boxes …

Performing language-conditioned robotic manipulation tasks in unstructured environments
is highly demanded for general intelligent robots. Conventional robotic manipulation …

In autonomous driving predicting future events in advance and evaluating the foreseeable
risks empowers autonomous vehicles to plan their actions enhancing safety and efficiency …

In contrast to extensive studies on general vision pre-training for scalable visual
autonomous driving remains seldom explored. Visual autonomous driving applications …

Multi-agent pathfinding (MAPF) is a critical field in many large-scale robotic applications,
often being the fundamental step in multi-agent systems. The increasing complexity of MAPF …

Autonomous driving technology, a catalyst for revolutionizing transportation and urban
mobility, has the tend to transition from rule-based systems to data-driven strategies …

The field of autonomous driving increasingly demands high-quality annotated training data.
In this paper we propose Panacea an innovative approach to generate panoramic and …

Diffusion models (DMs) have enabled breakthroughs in image synthesis tasks but lack an
intuitive interface for consistent image-to-image (I2I) translation. Various methods have been …

Scene simulation in autonomous driving has gained significant attention because of its huge
potential for generating customized data. However existing editable scene simulation …