6 accepted papers
The perception of deformable linear objects (DLOs) poses significant challenges in robotic manipulation. Crossovers, mergings, and bifurcations of multiple DLOs complicate the identification of individual DLO physical instances. Furthermore, DLOs are often too large to be captured by a single camera…
Existing concept customization methods have achieved remarkable outcomes in high-fidelity and multi-concept customization. However, they often neglect the influence on the original model's behavior and capabilities when learning new personalized concepts. To address this issue, we propose PureCC. Pu
The perception of deformable linear objects (DLOs) poses significant challenges in robotic manipulation. Crossovers, mergings, and bifurcations of multiple DLOs complicate the identification of individual DLO physical instances. Furthermore, DLOs are often too large to be captured by a single camera
Parametric point clouds are sampled from CAD shapes and are becoming increasingly common in industrial manufacturing. Most CAD-specific deep learning methods focus on geometric features, while overlooking constraints inherent in CAD shapes. This limits their ability to discern CAD shapes with simila…
Predicting the plausible future paths of pedestrians is essential for human-involved applications (e.g., autonomous driving and service robotics). Existing pedestrian trajectory prediction methods mainly focus on the performance of multi-scene trained models in single-scene tests, neglecting the cro
Grasping an object when it is in an ungraspable pose is a challenging task, such as books or other large flat objects placed horizontally on a table. Inspired by human manipulation, we address this problem by pushing the object to the edge of the table and then grasping it from the hanging part. In…