4 accepted papers
Loop closing is critically important in Simultaneous Localization and Mapping (SLAM) due to its ability to correct accumulated localization errors. However, existing methods are hindered by the difficulty of acquiring pose labels and the unreliability of ground truth data. In this paper, we propose
In the realm of robotics, teleoperation plays a pivotal role in performing high-risk or intricate tasks, and obtaining precise 3D whole-body pose is crucial for this purpose. Traditional two-stage methods have limitations in estimating different body parts, leading to complex systems and higher esti
Federated learning research has recently shifted from Convolutional Neural Networks (CNNs) to Vision Transformers (ViTs) due to their superior capacity. ViTs training demands higher computational resources due to the lack of 2D inductive biases inherent in CNNs. However, efficient federated training…
Vision-Language-Action (VLA) models have significantly advanced robotic manipulation by enabling robots to interpret language instructions for task execution. However, training these models often relies on large-scale user-specific data, raising concerns about privacy and security, which in turn lim…