We consider the task of generating realistic 3D shapes, which is useful ...
A fundamental task in robotics is to navigate between two locations. In
...
Building 3D maps of the environment is central to robot navigation, plan...
An effective framework for learning 3D representations for perception ta...
To integrate high amounts of renewable energy resources, electrical powe...
Estimating the uncertainty in deep neural network predictions is crucial...
Driving SMARTS is a regular competition designed to tackle problems caus...
Humans are remarkably good at understanding and reasoning about complex
...
This paper considers the problem of unsupervised 3D object reconstructio...
3D scene graphs (3DSGs) are an emerging description; unifying symbolic,
...
In this work, we consider the problem of learning a perception model for...
In model-free deep reinforcement learning (RL) algorithms, using noisy v...
In this paper, we consider the problem of iterative machine teaching, wh...
The ability for a robot to navigate with only the use of vision is appea...
In many situations it is either impossible or impractical to develop and...
While modern deep neural networks are performant perception modules,
per...
Heteroscedastic regression is the task of supervised learning where each...
As robotics matures and increases in complexity, it is more necessary th...
The continual learning problem involves training models with limited cap...
We show that the sum of the implicit generator log-density log p_g of a
...
Gradient-based meta-learners such as Model-Agnostic Meta-Learning (MAML)...
Goal-directed Reinforcement Learning (RL) traditionally considers an age...
The question of "representation" is central in the context of dense
simu...
Modern generative models are usually designed to match target distributi...
Domain randomization is a popular technique for improving domain transfe...
Despite recent breakthroughs, the ability of deep learning and reinforce...
Active localization is the problem of generating robot actions that allo...
Learning effective visuomotor policies for robots purely from data is
ch...
With the success of deep learning based approaches in tackling challengi...