4 accepted papers
Achieving human-like dexterous manipulation is essential for general-purpose robots but remains a challenge. Recent advances in Vision-Language-Action (VLA) models offer the potential to learn flexible skills from demonstration data. However, training effective VLAs requires a large amount of high-q…
Learning to navigate in dynamic and complex open-world environments is a critical yet challenging capability for autonomous robots. Existing approaches often rely on cascaded modular frameworks, which require extensive hyperparameter tuning or learning from limited real-world demonstration data. In …
Shape restoration aims to recover intact 3D shapes from defective ones, such as those that are incomplete, noisy, and low-resolution. Previous works have achieved impressive results in shape restoration subtasks thanks to advanced generative models. While effective for specific shape defects, they a…
Semi-supervised domain generalization (SSDG) aims to build a domain-generalized model using partially labeled data from source domains. Mainstream SSDG methods follow the augmentation consistency in FixMatch. However, the extraction of domain-invariant features may be challenging due to the absence…