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Found 17 papers, 4 papers with code
RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches
Generalization remains one of the most important desiderata for robust robot learning systems.
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web.
Open-World Object Manipulation using Pre-trained Vision-Language Models
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.
PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale
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.
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
Imitation learning allows agents to learn complex behaviors from demonstrations.
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
Imitation learning allows agents to learn complex behaviors from demonstrations.
Generalized Feedback Loop for Joint Hand-Object Pose Estimation
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.
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks
Using domain adaptation methods to cross this "reality gap" requires a large amount of unlabelled real-world data, whilst domain randomization alone can waste modeling power.
On Pre-Trained Image Features and Synthetic Images for Deep Learning
Deep Learning methods usually require huge amounts of training data to perform at their full potential, and often require expensive manual labeling.
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
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.
Training a Feedback Loop for Hand Pose Estimation
We propose an entirely data-driven approach to estimating the 3D pose of a hand given a depth image.
Efficiently Creating 3D Training Data for Fine Hand Pose Estimation
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.
Hands Deep in Deep Learning for Hand Pose Estimation
We introduce and evaluate several architectures for Convolutional Neural Networks to predict the 3D joint locations of a hand given a depth map.
Learning Descriptors for Object Recognition and 3D Pose Estimation
Detecting poorly textured objects and estimating their 3D pose reliably is still a very challenging problem.
Accurate Object Detection with Joint Classification-Regression Random Forests
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.
Alternating Decision Forests
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.
Optimizing 1-Nearest Prototype Classifiers
The development of complex, powerful classifiers and their constant improvement have contributed much to the progress in many fields of computer vision.
