6 papers
Affordance-Graphed Task Worlds: Self-Evolving Task Generation for Scalable Embodied Learning
Xiang Liu, Sen Cui, Guocai Yao +4
Training robotic policies directly in the real world is expensive and unscalable. Although generative simulation enables large-scale data synthesis, current approaches often fail t…
nuCarla: A nuScenes-Style Bird's-Eye View Perception Dataset for CARLA Simulation
Zhijie Qiao, Zhong Cao, Henry X. Liu
End-to-end (E2E) autonomous driving heavily relies on closed-loop simulation, where perception, planning, and control are jointly trained and evaluated in interactive environments.…
Boosting Zero-Shot VLN via Abstract Obstacle Map-Based Waypoint Prediction with TopoGraph-and-VisitInfo-Aware Prompting
Boqi Li, Siyuan Li, Weiyi Wang +3
With the rapid progress of foundation models and robotics, vision-language navigation (VLN) has emerged as a key task for embodied agents with broad practical applications. We addr…
End2Race: Efficient End-to-End Imitation Learning for Real-Time F1Tenth Racing
Zhijie Qiao, Haowei Li, Zhong Cao +1
F1Tenth is a widely adopted reduced-scale platform for developing and testing autonomous racing algorithms, hosting annual competitions worldwide. With high operating speeds, dynam…
LightEMMA: Lightweight End-to-End Multimodal Model for Autonomous Driving
Zhijie Qiao, Haowei Li, Zhong Cao +1
Vision-Language Models (VLMs) have demonstrated significant potential for end-to-end autonomous driving. However, the field still lacks a practical platform that enables dynamic mo…
RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion
Jiawei Wang, Xintao Yan, Yao Mu +3
Generating safety-critical scenarios in high-fidelity simulations offers a promising and cost-effective approach for efficient testing of autonomous vehicles. Existing methods typi…