We investigate whether Deep Reinforcement Learning (Deep RL) is able to
...
While dense visual SLAM methods are capable of estimating dense
reconstr...
Offline Reinforcement Learning (ORL) enablesus to separately study the t...
Off-policy reinforcement learning for control has made great strides in ...
Modern Reinforcement Learning (RL) algorithms promise to solve difficult...
Modern reinforcement learning algorithms can learn solutions to increasi...
Generally capable Spatial AI systems must build persistent scene
represe...
Systems which incrementally create 3D semantic maps from image sequences...
Systems which incrementally create 3D semantic maps from image sequences...
We propose a new multi-instance dynamic RGB-D SLAM system using an
objec...
The extraction and matching of interest points is a prerequisite for vis...
Much like humans, robots should have the ability to leverage knowledge f...
Sum-of-squares objective functions are very popular in computer vision
a...
We propose an online object-level SLAM system which builds a persistent ...
The representation of geometry in real-time 3D perception systems contin...
Over the last decades quaternions have become a crucial and very success...
We introduce gvnn, a neural network library in Torch aimed towards bridg...