Vision-Language-Action (VLA) models for autonomous driving rely heavily on successful expert demonstrations, leaving model-specific failures underexploited. Learning from these failures is hindered by unreliable diagnoses, poorly matched correction targets, and coarse rewards. We propose RefineDrive, a failure-guided p...
Zhe Sun, Zi-Yin Luo, Ye-Hao Lu et al.· 0 citations
Average performance on routine driving benchmarks does not establish planner reliability under rare, safety-critical hazards. We proposed ExceptionDrive, a counterfactual planning benchmark that uses VLM-assisted screening, localized multi-view editing, and quality auditing to insert hazards into real nuScenes scenes w...
Zi-Yin Luo, Zhe Sun, Ye-Hao Lu et al.· 0 citations
Reliable perception is essential for underwater vehicles operating in complex environments, where light attenuation and scattering often degrade visibility and compromise optical sensing. Forward-looking sonar (FLS) offers an alternative by providing high-frame-rate acoustic imaging under poor optical conditions. Howev...
Si-Yuan Du, Kan-Zhong Yao, You-Dong Wang et al.· 0 citations
Robust object 6D pose tracking is critical for robotic systems operating in dynamic and occluded scenes. Per-frame estimators are accurate but computationally expensive, while current trackers struggle with fast motion and complete occlusion due to their reliance on continuous visibility. To address these challenges, w...
Jun-Yue Li, Ye Zheng, Yi-Fan Chen et al.· arXiv.org· 0 citations
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