129 citations · 211 across the 4 of their papers we have counts for
7 papers
The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels
Austin Stone, Oscar Ramirez, Kurt Konolige +1
Robots have to face challenging perceptual settings, including changes in viewpoint, lighting, and background. Current simulated reinforcement learning (RL) benchmarks such as DM C…
What Matters in Unsupervised Optical Flow
Rico Jonschkowski, Austin Stone, Jonathan T. Barron +3
We systematically compare and analyze a set of key components in unsupervised optical flow to identify which photometric loss, occlusion handling, and smoothness regularization is…
KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects
Xingyu Liu, Rico Jonschkowski, Anelia Angelova +1
Estimating the 3D pose of desktop objects is crucial for applications such as robotic manipulation. Many existing approaches to this problem require a depth map of the object for b…
Deep Dynamics Models for Learning Dexterous Manipulation
Anusha Nagabandi, Kurt Konoglie, Sergey Levine +1
Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills. However, many of the more complex behaviors are also not…
On Pre-Trained Image Features and Synthetic Images for Deep Learning
Stefan Hinterstoisser, Vincent Lepetit, Paul Wohlhart +1
Deep Learning methods usually require huge amounts of training data to perform at their full potential, and often require expensive manual labeling. Using synthetic images is there…
Going Further with Point Pair Features
Stefan Hinterstoisser, Vincent Lepetit, Naresh Rajkumar +1
Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce no…