2 citations · 2 across the 7 of their papers we have counts for
8 papers
GeoWorldAD: Geometry World Action Model for Autonomous Driving
Songyan Zhang, Jinyuan Tian, Hanbing Li +9
Autonomous driving requires both safe and efficient planning decisions in dynamic 3D environments. Although recent Vision/Video-Action models learn policies directly from visual ob…
SpaAct: Spatially-Activated Transition Learning with Curriculum Adaptation for Vision-Language Navigation
Pengna Li, Kangyi Wu, Shaoqing Xu +7
Vision-and-Language Navigation (VLN) aims to enable an embodied agent to follow natural-language instructions and navigate to a target location in unseen 3D environments. We argue…
DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scale
Sicheng Zuo, Zixun Xie, Wenzhao Zheng +6
End-to-end autonomous driving has evolved from the conventional paradigm based on sparse perception into vision-language-action (VLA) models, which focus on learning language descr…
OpenGo: An OpenClaw-Based Robotic Dog with Real-Time Skill Switching
Hanbing Li, Xuewei Cao, Zhiwen Zeng +3
Adaptation to complex tasks and multiple scenarios remains a significant challenge for a single robot agent. The ability to acquire organize, and switch between a wide range of ski…
VILTA: A VLM-in-the-Loop Adversary for Enhancing Driving Policy Robustness
Qimao Chen, Fang Li, Shaoqing Xu +9
The safe deployment of autonomous driving (AD) systems is fundamentally hindered by the long-tail problem, where rare yet critical driving scenarios are severely underrepresented i…
MiMo-Embodied: X-Embodied Foundation Model Technical Report
Xiaoshuai Hao, Lei Zhou, Zhijian Huang +41
We open-source MiMo-Embodied, the first cross-embodied foundation model to successfully integrate and achieve state-of-the-art performance in both Autonomous Driving and Embodied A…
Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards
Ming Li, Pei Chen, Zhenhao Zhang +10
Large Language Models demonstrate strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC), a degradation in performance as information i…
NEUSIS: A Compositional Neuro-Symbolic Framework for Autonomous Perception, Reasoning, and Planning in Complex UAV Search Missions
Zhixi Cai, Cristian Rojas Cardenas, Kevin Leo +14
This paper addresses the problem of autonomous UAV search missions, where a UAV must locate specific Entities of Interest (EOIs) within a time limit, based on brief descriptions in…