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Found 5 papers, 0 papers with code
Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Picking
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.
Uncertainty-driven Exploration Strategies for Online Grasp Learning
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.
Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking
This paper presents a novel method for model-free prediction of grasp poses for suction grippers with multiple suction cups.
Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications
In this paper we show that given a 3D model of an object, we can generate its descriptor space image, which allows for supervised training of DONs.
Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks
Furthermore, to accomplish complex manipulation tasks, robots should be able to sequence several skills and adapt them to changing situations.
