most citedEmbodied Understanding of Driving Scenarios

2 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2025★ 1 cited

AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems

AgiBot World Contributors, AgiBot-World-Contributors, Qingwen Bu +50

We explore how scalable robot data can address real-world challenges for generalized robotic manipulation. Introducing AgiBot World, a large-scale platform comprising over 1 millio…

cs.RO2024★ 2 cited

Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation

Qingwen Bu, Hongyang Li, Li Chen +5

The increasing demand for versatile robotic systems to operate in diverse and dynamic environments has emphasized the importance of a generalist policy, which leverages a large cro…

cs.RO2024

Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving

Haochen Liu, Li Chen, Yu Qiao +2

Autonomous driving system aims for safe and social-consistent driving through the behavioral integration among interactive agents. However, challenges remain due to multi-agent sce…

cs.RO2024

Learning Manipulation by Predicting Interaction

Jia Zeng, Qingwen Bu, Bangjun Wang +12

Representation learning approaches for robotic manipulation have boomed in recent years. Due to the scarcity of in-domain robot data, prevailing methodologies tend to leverage larg…

cs.CV2024★ 2 cited

Embodied Understanding of Driving Scenarios

Yunsong Zhou, Linyan Huang, Qingwen Bu +7

Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded…

cs.CV2024

GenAD: Generalized Predictive Model for Autonomous Driving

Jiazhi Yang, Shenyuan Gao, Yihang Qiu +11

In this paper, we introduce the first large-scale video prediction model in the autonomous driving discipline. To eliminate the restriction of high-cost data collection and empower…