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FedDRMan, a federated subspace-guided distillation framework for heterogeneous robot manipulation, substantially improves knowledge transfer and consistently outperforms strong federated baselines achieving a peak mean success rate of 80.7%.
Results show that GFM-derived geometry can provide a persistent structural prior for VPR when visual appearance becomes unreliable, with the largest gains under reverse traversal and long-term appearance variation.
TOLiD is proposed, a self-supervised pretraining method for LiDAR representation learning that addresses the gap between dense ViT token representations and sparse 3D encoders by coupling a LiDAR backbone with a student Vision Transformer initialized from a frozen VFM teacher and applying supervision over compatible pa...
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