5 accepted papers
Multi-embodiment grasping aims to develop approaches that exhibit generalist behavior across diverse gripper designs. Existing methods often learn the gripper kinematic structure implicitly and face challenges due to the difficulty of sourcing the required large-scale data. In this work, we present
Grasping is a fundamental skill in robotics with diverse applications. However, current approaches for predicting successful grasps are often tailored to specific grippers, limiting their applicability when gripper designs change. To address this limitation, we explore the transfer of grasping strat
Bin picking is an important building block for many robotic systems, in logistics, production or in household use-cases. In recent years, machine learning methods for the prediction of 6-DoF grasps on diverse and unknown objects have shown promising progress. However, existing approaches only consid…
The prevailing grasp prediction methods predominantly rely on offline learning, overlooking the dynamic grasp learning that occurs during real-time adaptation to novel picking scenarios. These scenarios may involve previously unseen objects, variations in camera perspectives, and bin configurations,…
Existing grasp prediction approaches are mostly based on offline learning, while, ignoring the exploratory grasp learning during online adaptation to new picking scenarios, i.e., objects that are unseen or out-of-domain (OOD), camera and bin settings, etc. In this paper, we present an uncertainty-ba…