Reliable execution of long-horizon mobile manipulation tasks remains challenging because overall task success depends on the successful completion of multiple constituent skills. Existing benchmarks, however, still rely primarily on full-task rollouts and aggregate task-level metrics, making intermediate failures diffi...
Chun-Yun Ma, Lun Luo, Xing-Jian Luo et al.· 0 citations
Open-world instance classification in LiDAR point clouds aims to discover and categorize instances of unknown classes unseen during training, without access to their ground-truth labels. The task is extremely challenging since only known-class labels are available for training. Existing LiDAR classification methods oft...
Wenbang Deng, Zi-Yue Wang, Wen-Qing Kuang et al.· IEEE Transactions on Automat...· 0 citations
LiDAR-based collaborative perception can mitigate occlusions by exchanging complementary viewpoints via Vehicle-to-Everything (V2X) communication. However, existing methods often depend heavily 3D annotations and adopt a pretraining pipeline that reconstruction serves as initialization. This letter presents a unified m...
Benwu Wang, Xu Li, Xieyuanli Chen et al.· IEEE Robotics and Automation...· 0 citations
Trajectory prediction of surrounding agents is a prerequisite for safe planning and decision making in autonomous driving. Without high-definition (HD) maps, sensor-derived bird's-eye-view (BEV) features provide no explicit lane topology or drivable-area priors, making it inherently difficult to ground each agent in it...
Fei-Fei Liu, Zejun Wei, Haozhe Wang et al.· 0 citations
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