30 citations · 43 across the 13 of their papers we have counts for
7 papers · 1 filter
Deep Modeling of Non-Gaussian Aleatoric Uncertainty
Aastha Acharya, Caleb Lee, Marissa D'Alonzo +3
Deep learning offers promising new ways to accurately model aleatoric uncertainty in robotic state estimation systems, particularly when the uncertainty distributions do not confor…
Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories
Aastha Acharya, Rebecca Russell, Nisar R. Ahmed
Giving autonomous agents the ability to forecast their own outcomes and uncertainty will allow them to communicate their competencies and be used more safely. We accomplish this by…
Symmetry Detection in Trajectory Data for More Meaningful Reinforcement Learning Representations
Marissa D'Alonzo, Rebecca Russell
Knowledge of the symmetries of reinforcement learning (RL) systems can be used to create compressed and semantically meaningful representations of a low-level state space. We prese…
Learning and Understanding a Disentangled Feature Representation for Hidden Parameters in Reinforcement Learning
Christopher Reale, Rebecca Russell
Hidden parameters are latent variables in reinforcement learning (RL) environments that are constant over the course of a trajectory. Understanding what, if any, hidden parameters…
Competency Assessment for Autonomous Agents using Deep Generative Models
Aastha Acharya, Rebecca Russell, Nisar R. Ahmed
For autonomous agents to act as trustworthy partners to human users, they must be able to reliably communicate their competency for the tasks they are asked to perform. Towards thi…
Explaining Conditions for Reinforcement Learning Behaviors from Real and Imagined Data
Aastha Acharya, Rebecca Russell, Nisar R. Ahmed
The deployment of reinforcement learning (RL) in the real world comes with challenges in calibrating user trust and expectations. As a step toward developing RL systems that are ab…
Multivariate Uncertainty in Deep Learning
Rebecca L. Russell, Christopher Reale
Deep learning has the potential to dramatically impact navigation and tracking state estimation problems critical to autonomous vehicles and robotics. Measurement uncertainties in…