1 citations · 1 across the 9 of their papers we have counts for
9 papers
Simplifying Flow Matching Transformations with Low-Rank Mixture Models
Liam A. Kruse, Houjun Liu, Alexandros E. Tzikas +2
Normalizing flows are powerful generative models that learn an invertible mapping between complex data distributions and simple latent distributions, typically a standard normal de…
Foundational World Models Accurately Detect Bimanual Manipulator Failures
Isaac R. Ward, Michelle Ho, Houjun Liu +7
Deploying visuomotor robots at scale is challenging due to the potential for anomalous failures to degrade performance, cause damage, or endanger human life. Bimanual manipulators…
Scalable Importance Sampling in High Dimensions with Low-Rank Mixture Proposals
Liam A. Kruse, Marc R. Schlichting, Mykel J. Kochenderfer
Importance sampling is a Monte Carlo technique for efficiently estimating the likelihood of rare events by biasing the sampling distribution towards the rare event of interest. By…
Enhanced Importance Sampling through Latent Space Exploration in Normalizing Flows
Liam A. Kruse, Alexandros E. Tzikas, Harrison Delecki +2
Importance sampling is a rare event simulation technique used in Monte Carlo simulations to bias the sampling distribution towards the rare event of interest. By assigning appropri…
Sliced Distribution Matching based on Cumulative Distribution Functions with Applications to Control
Alexandros E. Tzikas, Arec Jamgochian, Nazim Kemal Ure +2
Computing the similarity between two probability distributions is a recurring theme across control. We introduce a unified family of distances between the probability distributions…
Deep Normalizing Flows for State Estimation
Harrison Delecki, Liam A. Kruse, Marc R. Schlichting +1
Safe and reliable state estimation techniques are a critical component of next-generation robotic systems. Agents in such systems must be able to reason about the intentions and tr…
Uncertainty-Aware Online Merge Planning with Learned Driver Behavior
Liam A. Kruse, Esen Yel, Ransalu Senanayake +1
Safe and reliable autonomy solutions are a critical component of next-generation intelligent transportation systems. Autonomous vehicles in such systems must reason about complex a…
Dyadic Sex Composition and Task Classification Using fNIRS Hyperscanning Data
Liam A. Kruse, Allan L. Reiss, Mykel J. Kochenderfer +1
Hyperscanning with functional near-infrared spectroscopy (fNIRS) is an emerging neuroimaging application that measures the nuanced neural signatures underlying social interactions.…
A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering
Raunak Bhattacharyya, Soyeon Jung, Liam Kruse +2
Autonomous vehicles need to model the behavior of surrounding human driven vehicles to be safe and efficient traffic participants. Existing approaches to modeling human driving beh…