3 papers
cs.RO2025
HANDO: Hierarchical Autonomous Navigation and Dexterous Omni-loco-manipulation
Jingyuan Sun, Chaoran Wang, Mingyu Zhang +6
Seamless loco-manipulation in unstructured environments requires robots to leverage autonomous exploration alongside whole-body control for physical interaction. In this work, we i…
cs.RO2025
FedVLA: Federated Vision-Language-Action Learning with Dual Gating Mixture-of-Experts for Robotic Manipulation
Cui Miao, Tao Chang, Meihan Wu +4
Vision-language-action (VLA) models have significantly advanced robotic manipulation by enabling robots to interpret language instructions for task execution. However, training the…
cs.CV2024
EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients
Meihan Wu, Tao Chang, Cui Miao +5
Federated learning research has recently shifted from Convolutional Neural Networks (CNNs) to Vision Transformers (ViTs) due to their superior capacity. ViTs training demands highe…