Search Results for author: Paul Wohlhart

Found 17 papers, 4 papers with code

Open-World Object Manipulation using Pre-trained Vision-Language Models

no code implementations2 Mar 2023 Austin Stone, Ted Xiao, Yao Lu, Keerthana Gopalakrishnan, Kuang-Huei Lee, Quan Vuong, Paul Wohlhart, Sean Kirmani, Brianna Zitkovich, Fei Xia, Chelsea Finn, Karol Hausman

This brings up a notably difficult challenge for robots: while robot learning approaches allow robots to learn many different behaviors from first-hand experience, it is impractical for robots to have first-hand experiences that span all of this semantic information.

Language Modelling

PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale

no code implementations15 Oct 2022 Kuang-Huei Lee, Ted Xiao, Adrian Li, Paul Wohlhart, Ian Fischer, Yao Lu

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 learning agents, as the ability to model what will happen next is critical to success on many control tasks.

reinforcement-learning Reinforcement Learning (RL) +2

Generalized Feedback Loop for Joint Hand-Object Pose Estimation

no code implementations25 Mar 2019 Markus Oberweger, Paul Wohlhart, Vincent Lepetit

We show that we can correct the mistakes made by a Convolutional Neural Network trained to predict an estimate of the 3D pose by using a feedback loop.

3D Hand Pose Estimation hand-object pose

On Pre-Trained Image Features and Synthetic Images for Deep Learning

no code implementations29 Oct 2017 Stefan Hinterstoisser, Vincent Lepetit, Paul Wohlhart, Kurt Konolige

Deep Learning methods usually require huge amounts of training data to perform at their full potential, and often require expensive manual labeling.

Object Recognition

Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping

1 code implementation22 Sep 2017 Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, Sergey Levine, Vincent Vanhoucke

We extensively evaluate our approaches with a total of more than 25, 000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN.

Domain Adaptation Industrial Robots +2

Training a Feedback Loop for Hand Pose Estimation

no code implementations ICCV 2015 Markus Oberweger, Paul Wohlhart, Vincent Lepetit

We propose an entirely data-driven approach to estimating the 3D pose of a hand given a depth image.

Hand Pose Estimation

Efficiently Creating 3D Training Data for Fine Hand Pose Estimation

1 code implementation CVPR 2016 Markus Oberweger, Gernot Riegler, Paul Wohlhart, Vincent Lepetit

While many recent hand pose estimation methods critically rely on a training set of labelled frames, the creation of such a dataset is a challenging task that has been overlooked so far.

Hand Pose Estimation

Hands Deep in Deep Learning for Hand Pose Estimation

1 code implementation24 Feb 2015 Markus Oberweger, Paul Wohlhart, Vincent Lepetit

We introduce and evaluate several architectures for Convolutional Neural Networks to predict the 3D joint locations of a hand given a depth map.

Hand Pose Estimation

Accurate Object Detection with Joint Classification-Regression Random Forests

no code implementations CVPR 2014 Samuel Schulter, Christian Leistner, Paul Wohlhart, Peter M. Roth, Horst Bischof

In this way, we can simultaneously predict the object probability of a window in a sliding window approach as well as regress its aspect ratio with a single model.

Classification General Classification +3

Alternating Decision Forests

no code implementations CVPR 2013 Samuel Schulter, Paul Wohlhart, Christian Leistner, Amir Saffari, Peter M. Roth, Horst Bischof

Contrary to Boosted Trees, in our method the loss minimization is an inherent part of the tree growing process, thus allowing to keep the benefits of common Random Forests, such as, parallel processing.

object-detection Object Detection

Optimizing 1-Nearest Prototype Classifiers

no code implementations CVPR 2013 Paul Wohlhart, Martin Kostinger, Michael Donoser, Peter M. Roth, Horst Bischof

The development of complex, powerful classifiers and their constant improvement have contributed much to the progress in many fields of computer vision.

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