95 citations · 184 across the 32 of their papers we have counts for
6 papers · 1 filter
Active and Transfer Learning of Grasps by Kernel Adaptive MCMC
Philipp Zech, Hanchen Xiong, Justus Piater
Human ability of both versatile grasping of given objects and grasping of novel (as of yet unseen) objects is truly remarkable. This probably arises from the experience infants gat…
Active and Transfer Learning of Grasps by Sampling from Demonstration
Philipp Zech, Justus Piater
We guess humans start acquiring grasping skills as early as at the infant stage by virtue of two key processes. First, infants attempt to learn grasps for known objects by imitatin…
Grasp Learning by Sampling from Demonstration
Philipp Zech, Justus Piater
Robotic grasping traditionally relies on object features or shape information for learning new or applying already learned grasps. We argue however that such a strong reliance on o…
25 years of CNNs: Can we compare to human abstraction capabilities?
Sebastian Stabinger, Antonio Rodríguez-Sánchez, Justus Piater
We try to determine the progress made by convolutional neural networks over the past 25 years in classifying images into abstractc lasses. For this purpose we compare the performan…
Learning Abstract Classes using Deep Learning
Sebastian Stabinger, Antonio Rodriguez-Sanchez, Justus Piater
Humans are generally good at learning abstract concepts about objects and scenes (e.g.\ spatial orientation, relative sizes, etc.). Over the last years convolutional neural network…
Robotic Playing for Hierarchical Complex Skill Learning
Simon Hangl, Emre Ugur, Sandor Szedmak +1
In complex manipulation scenarios (e.g. tasks requiring complex interaction of two hands or in-hand manipulation), generalization is a hard problem. Current methods still either re…