15 citations · 27 across the 8 of their papers we have counts for
8 papers
iFLYTEK-Embodied-Omni Technical Report
Yuan Zhang, Jingfei Ni, Guanchen Lu +12
General-purpose embodied agents must understand multimodal instructions, anticipate how their environment will evolve, and produce precise control actions over extended horizons. E…
GEAR-VLA: Learning Geometry-Aware Action Representations for Generalizable Robotic Manipulation
Yuan Zhang, Shiqi Zhang, Yedong Shen +11
Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot emb…
ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation
Shiwei Feng, Yapeng Ye, Qingkai Shi +5
As Autonomous driving systems (ADS) have transformed our daily life, safety of ADS is of growing significance. While various testing approaches have emerged to enhance the ADS reli…
EffiTune: Diagnosing and Mitigating Training Inefficiency for Parameter Tuner in Robot Navigation System
Shiwei Feng, Xuan Chen, Zikang Xiong +5
Robot navigation systems are critical for various real-world applications such as delivery services, hospital logistics, and warehouse management. Although classical navigation met…
Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks
Zhiyuan Cheng, Cheng Han, James Liang +3
Monocular Depth Estimation (MDE) plays a vital role in applications such as autonomous driving. However, various attacks target MDE models, with physical attacks posing significant…
Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detection
Zhiyuan Cheng, Hongjun Choi, James Liang +5
Multi-sensor fusion (MSF) is widely used in autonomous vehicles (AVs) for perception, particularly for 3D object detection with camera and LiDAR sensors. The purpose of fusion is t…
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks
Zhiyuan Cheng, James Liang, Guanhong Tao +2
Monocular Depth Estimation (MDE) is a critical component in applications such as autonomous driving. There are various attacks against MDE networks. These attacks, especially the p…
Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches
Zhiyuan Cheng, James Liang, Hongjun Choi +4
Deep learning has substantially boosted the performance of Monocular Depth Estimation (MDE), a critical component in fully vision-based autonomous driving (AD) systems (e.g., Tesla…