When we rely on deep-learned models for robotic perception, we must reco...
A critical task for developing safe autonomous driving stacks is to dete...
As robots acquire increasingly sophisticated skills and see increasingly...
When testing conditions differ from those represented in training data,
...
When deploying modern machine learning-enabled robotic systems in high-s...
In this paper we present a trade study-based method to optimize the
arch...
Evaluating the safety of an autonomous vehicle (AV) depends on the behav...
Motion planning for a multi-limbed climbing robot must consider the robo...
As input distributions evolve over a mission lifetime, maintaining
perfo...
Very high dimensional nonlinear systems arise in many engineering proble...
Modeling and control of high-dimensional, nonlinear robotic systems rema...
To enable safe autonomous vehicle (AV) operations, it is critical that a...
Robust motion planning entails computing a global motion plan that is sa...
In this work we derive a second-order approach to bilevel optimization, ...
Verifying that input-output relationships of a neural network conform to...
We identify an issue in recent approaches to learning-based control that...
When deploying machine learning models in high-stakes robotics applicati...
As safety-critical autonomous vehicles (AVs) will soon become pervasive ...
Action anticipation, intent prediction, and proactive behavior are all
d...
Human behavior prediction models enable robots to anticipate how humans ...
Sampling-based motion planning techniques have emerged as an efficient
a...
RRT* is one of the most widely used sampling-based algorithms for
asympt...
Action anticipation, intent prediction, and proactive behavior are all
d...
This work presents a methodology for modeling and predicting human behav...
This paper presents a method for constructing human-robot interaction
po...