7.8k citations · 12.8k across the 52 of their papers we have counts for
5 papers · 2 filters
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan +6
Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation to produce large amou…
From pixels to percepts: Highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor
Nathan F. Lepora, Alex Church, Conrad De Kerckhove +2
Deep learning has the potential to have the impact on robot touch that it has had on robot vision. Optical tactile sensors act as a bridge between the subjects by allowing techniqu…
Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal
Jake Bruce, Niko Sünderhauf, Piotr Mirowski +2
Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large…
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…
Reinforcement and Imitation Learning for Diverse Visuomotor Skills
Yuke Zhu, Ziyu Wang, Josh Merel +8
We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent. We apply this approach to r…