3 papers
cs.RO2025
Using Temperature Sampling to Effectively Train Robot Learning Policies on Imbalanced Datasets
Basavasagar Patil, Sydney Belt, Jayjun Lee +2
Increasingly large datasets of robot actions and sensory observations are being collected to train ever-larger neural networks. These datasets are collected based on tasks and whil…
cs.LG2025
How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies
Akansha Kalra, Basavasagar Patil, Guanhong Tao +1
Imitation learning, also known as learning from demonstrations, is a popular approach to train AI models; however, the vulnerability of these models to adversarial attacks remains…
cs.NE2023
Univariate Radial Basis Function Layers: Brain-inspired Deep Neural Layers for Low-Dimensional Inputs
Daniel Jost, Basavasagar Patil, Xavier Alameda-Pineda +1
Deep Neural Networks (DNNs) became the standard tool for function approximation with most of the introduced architectures being developed for high-dimensional input data. However,…