Trajectory prediction modules are key enablers for safe and efficient
pl...
When testing conditions differ from those represented in training data,
...
Meta-learning or learning to learn is a popular approach for learning ne...
As input distributions evolve over a mission lifetime, maintaining
perfo...
We propose to take on the problem ofWord Sense Disambiguation (WSD). In
...
We identify an issue in recent approaches to learning-based control that...
As robotic systems move from highly structured environments to open worl...
In order to safely deploy Deep Neural Networks (DNNs) within the percept...
Safe deployment of autonomous robots in diverse environments requires ag...
Meta-learning is a promising strategy for learning to efficiently learn
...
Today's robotic systems are increasingly turning to computationally expe...
Planning under model uncertainty is a fundamental problem across many
ap...
Gaussian Process (GP) regression has seen widespread use in robotics due...
Model-free Reinforcement Learning (RL) offers an attractive approach to ...