3 citations · 7 across the 6 of their papers we have counts for
6 papers
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…
AAAI 2022 Fall Symposium: Lessons Learned for Autonomous Assessment of Machine Abilities (LLAAMA)
Nicholas Conlon, Aastha Acharya, Nisar Ahmed
Modern civilian and military systems have created a demand for sophisticated intelligent autonomous machines capable of operating in uncertain dynamic environments. Such systems ar…
Uncertainty Quantification for Competency Assessment of Autonomous Agents
Aastha Acharya, Rebecca Russell, Nisar R. Ahmed
For safe and reliable deployment in the real world, autonomous agents must elicit appropriate levels of trust from human users. One method to build trust is to have agents assess a…
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…