collaborators

8 papers

cs.CV2025

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.…

cs.RO2025

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…

cs.RO2025

End2Race: An End-to-End Learning Framework for Multi-Vehicle Autonomous Racing

Zhijie Qiao, Haowei Li, Zhong Cao +1

Autonomous racing serves as a compelling testbed for advancing autonomous vehicle systems. The 1/10-scale F1Tenth platform is widely adopted for education and research, with annual…

cs.RO2025

Imaginative World Modeling with Scene Graphs for Embodied Agent Navigation

Yue Hu, Junzhe Wu, Ruihan Xu +5

Semantic navigation requires an agent to navigate toward a specified target in an unseen environment. Employing an imaginative navigation strategy that predicts future scenes befor…

cs.LG2025

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…

cs.RO2025

LightEMMA: A Longitudinal Evaluation of Vision-Language Models for Autonomous Driving

Zhijie Qiao, Haowei Li, Zhong Cao +1

Rapid advances in vision-language models (VLMs) have generated growing interest in their application to autonomous driving. A prevailing assumption is that successive VLM generatio…

cs.DC2025

Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputation

Geon-Woo Kim, Donghyun Kim, Jeongyoon Moon +5

Graph Neural Networks (GNNs) have been widely adopted for their ability to compute expressive node representations in graph datasets. However, serving GNNs on large graphs is chall…

cs.RO2021

Efficient Placard Discovery for Semantic Mapping During Frontier Exploration

David Balaban, Harshavardhan Jagannathan, Henry Liu +1

Semantic mapping is the task of providing a robot with a map of its environment beyond the open, navigable space of traditional Simultaneous Localization and Mapping (SLAM) algorit…