6 papers
Movement Primitives in Robotics: A Comprehensive Survey
Nolan B. Gutierrez, Joseph M. Cloud, William J. Beksi
Biological systems exhibit a continuous stream of movements, consisting of sequential segments, that allow them to perform complex tasks in a creative and versatile fashion. This o…
A Human-Centered Approach for Bootstrapping Causal Graph Creation
Minh Q. Tram, Nolan B. Gutierrez, William J. Beksi
Causal inference, a cornerstone in disciplines such as economics, genomics, and medicine, is increasingly being recognized as fundamental to advancing the field of robotics. In par…
MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration
Zongyao Lyu, Nolan B. Gutierrez, William J. Beksi
Open-set recognition refers to the problem in which classes that were not seen during training appear at inference time. This requires the ability to identify instances of novel cl…
Evaluating Uncertainty Calibration for Open-Set Recognition
Zongyao Lyu, Nolan B. Gutierrez, William J. Beksi
Despite achieving enormous success in predictive accuracy for visual classification problems, deep neural networks (DNNs) suffer from providing overconfident probabilities on out-o…
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive Fields
Nolan B. Gutierrez, William J. Beksi
Thermal images model the long-infrared range of the electromagnetic spectrum and provide meaningful information even when there is no visible illumination. Yet, unlike imagery that…
An Uncertainty Estimation Framework for Probabilistic Object Detection
Zongyao Lyu, Nolan B. Gutierrez, William J. Beksi
In this paper, we introduce a new technique that combines two popular methods to estimate uncertainty in object detection. Quantifying uncertainty is critical in real-world robotic…