3 citations · 4 across the 2 of their papers we have counts for
6 papers
PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale
Kuang-Huei Lee, Ted Xiao, Adrian Li +3
The predictive information, the mutual information between the past and future, has been shown to be a useful representation learning auxiliary loss for training reinforcement lear…
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
Allan Zhou, Eric Jang, Daniel Kappler +7
Imitation learning allows agents to learn complex behaviors from demonstrations. However, learning a complex vision-based task may require an impractical number of demonstrations.…
Generalized Feedback Loop for Joint Hand-Object Pose Estimation
Markus Oberweger, Paul Wohlhart, Vincent Lepetit
We propose an approach to estimating the 3D pose of a hand, possibly handling an object, given a depth image. We show that we can correct the mistakes made by a Convolutional Neura…
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…
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…
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart +9
Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative…