3 citations · 3 across the 6 of their papers we have counts for
6 papers
Enhancing Exploration with Diffusion Policies in Hybrid Off-Policy RL: Application to Non-Prehensile Manipulation
Huy Le, Tai Hoang, Miroslav Gabriel +2
Learning diverse policies for non-prehensile manipulation is essential for improving skill transfer and generalization to out-of-distribution scenarios. In this work, we enhance ex…
Efficient End-to-End Detection of 6-DoF Grasps for Robotic Bin Picking
Yushi Liu, Alexander Qualmann, Zehao Yu +5
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 predictio…
Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Picking
Huy Le, Philipp Schillinger, Miroslav Gabriel +2
The prevailing grasp prediction methods predominantly rely on offline learning, overlooking the dynamic grasp learning that occurs during real-time adaptation to novel picking scen…
Uncertainty-driven Exploration Strategies for Online Grasp Learning
Yitian Shi, Philipp Schillinger, Miroslav Gabriel +4
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., o…
Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking
Philipp Schillinger, Miroslav Gabriel, Alexander Kuss +2
This paper presents a novel method for model-free prediction of grasp poses for suction grippers with multiple suction cups. Our approach is agnostic to the design of the gripper a…
Learning Dense Visual Descriptors using Image Augmentations for Robot Manipulation Tasks
Christian Graf, David B. Adrian, Joshua Weil +5
We propose a self-supervised training approach for learning view-invariant dense visual descriptors using image augmentations. Unlike existing works, which often require complex da…