211 citations · 432 across the 31 of their papers we have counts for
6 papers · 1 filter
Quantifying the Reality Gap in Robotic Manipulation Tasks
Jack Collins, David Howard, Jürgen Leitner
We quantify the accuracy of various simulators compared to a real world robotic reaching and interaction task. Simulators are used in robotics to design solutions for real world ha…
Coordinated Heterogeneous Distributed Perception based on Latent Space Representation
Timo Korthals, Jürgen Leitner, Ulrich Rückert
We investigate a reinforcement approach for distributed sensing based on the latent space derived from multi-modal deep generative models. Our contribution provides insights to the…
Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter
Douglas Morrison, Peter Corke, Jürgen Leitner
Camera viewpoint selection is an important aspect of visual grasp detection, especially in clutter where many occlusions are present. Where other approaches use a static camera pos…
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs
Vibhavari Dasagi, Robert Lee, Serena Mou +3
Current end-to-end deep Reinforcement Learning (RL) approaches require jointly learning perception, decision-making and low-level control from very sparse reward signals and high-d…
Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach
Douglas Morrison, Peter Corke, Jürgen Leitner
This paper presents a real-time, object-independent grasp synthesis method which can be used for closed-loop grasping. Our proposed Generative Grasping Convolutional Neural Network…
The Limits and Potentials of Deep Learning for Robotics
Niko Sünderhauf, Oliver Brock, Walter Scheirer +8
The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning com…