2 citations · 2 across the 9 of their papers we have counts for
10 papers
Which Trajectory Is Safer? Combining Rule and Learning for Safety Evaluation
Xuewen Luo, Linlian Jiang, Bo Yu +3
Collision rate remains a dominant metric for evaluating autonomous driving trajectory safety, but its binary nature obscures substantial safety differences among collision-free tra…
Who Responds When the Driver Is Gone? A Framework for Holistic Passenger Intent Understanding
Xuewen Luo, Ding Fan, Ruiqi Chen +5
As autonomous vehicles advance toward driverless mobility, understanding and responding to passenger needs and intentions becomes increasingly important in the absence of a human d…
EagleVLA: Towards Onboard Real-Time Robot Control via Foresight-Aligned Asynchronous Inference
Zebin Yang, Qi Wang, Yunhe Wang +6
Vision-Language-Action (VLA) models have achieved impressive performance on diverse embodied tasks. However, deploying VLA models on low-power onboard devices, such as the Jetson O…
CIVIC: End-to-End Sequence Compactness for Efficient Vision-Language Models
Fengze Yang, Bo Yu, Xuewen Luo +2
Vision-Language Models (VLMs) face severe memory and latency bottlenecks due to high-resolution visual tokens. While current token reduction methods theoretically save FLOPs, post-…
Adaptive Stopping for Multi-Turn LLM Reasoning
Xiaofan Zhou, Huy Nguyen, Bo Yu +2
Large Language Models (LLMs) increasingly rely on multi-turn reasoning and interaction, such as adaptive retrieval-augmented generation (RAG) and ReAct-style agents, to answer diff…
KnowDiffuser: A Knowledge-Guided Diffusion Planner with LLM Reasoning
Fan Ding, Xuewen Luo, Fengze Yang +4
Recent advancements in Language Models (LMs) have demonstrated strong semantic reasoning capabilities, enabling their application in high-level decision-making for autonomous drivi…
KEEP: A KV-Cache-Centric Memory Management System for Efficient Embodied Planning
Zebin Yang, Tong Xie, Baotong Lu +3
Memory-augmented Large Language Models (LLMs) have demonstrated remarkable capability for complex and long-horizon embodied planning. By keeping track of past experiences and envir…
StateLinFormer: Stateful Training Enhancing Long-term Memory in Navigation
Zhiyuan Chen, Yuxuan Zhong, Fan Wang +4
Effective navigation intelligence relies on long-term memory to support both immediate generalization and sustained adaptation. However, existing approaches face a dilemma: modular…
DaDu-E: Rethinking the Role of Large Language Model in Robotic Computing Pipeline
Wenhao Sun, Sai Hou, Zixuan Wang +6
Performing complex tasks in open environments remains challenging for robots, even when using large language models (LLMs) as the core planner. Many LLM-based planners are ineffici…
KARMA: Augmenting Embodied AI Agents with Long-and-short Term Memory Systems
Zixuan Wang, Bo Yu, Junzhe Zhao +6
Embodied AI agents responsible for executing interconnected, long-sequence household tasks often face difficulties with in-context memory, leading to inefficiencies and errors in t…