6 accepted papers
Deformable Linear Objects (DLOs), such as cables and ropes, pose significant challenges for robotic manipulation due to their high-dimensional state space, nonlinear deformation dynamics, and strong sensitivity to external forces. Cable routing tasks, in particular, are further complicated by geomet…
Grasping target objects is a fundamental skill for robotic manipulation, but in cluttered environments with stacked or occluded objects, a single-step grasp is often insufficient. To address this, previous work has introduced pushing as an auxiliary action to create graspable space. However, these m…
In human–robot interaction (HRI), maintaining a safe distance is the primary requirement. The flexibility and safety of HRI are often limited by the efficiency and accuracy of distance algorithms. This study proposes a fast and accurate method based on the Gilbert-Johnson-Keerthi (GJK) algorithm by
Learning robust and generalizable manipulation skills from few demonstrations remains a key challenge in robotics, with broad applications in industrial automation and service robotics. Although recent imitation learning methods have achieved impressive results, they often require a large amount of
Given the image collection of an object, we aim at building a real-time image-based pose estimation method, which requires neither its CAD model nor hours of object-specific training. Recent NeRF-based methods provide a promising solution by directly optimizing the pose from pixel loss between rende…
In comparison with most methods focusing on 3D rigid object recognition and manipulation, deformable objects are more common in our real life but attract less attention. Generally, most existing methods for deformable object manipulation suffer two issues, 1) Massive demonstration: repeating thousan…