Ezra Ameperosa — accepted AI-conference papers

2025

RoCoDA: Counterfactual Data Augmentation for Data-Efficient Robot Learning from Demonstrations

Ezra Ameperosa, Jeremy A. Collins, Mrinal Jain, Animesh Garg

ICRA 2025

Imitation learning in robotics faces significant challenges in generalization due to the complexity of robotic environments and the high cost of data collection. We introduce RoCoDA, a novel method that unifies the concepts of invariance, equivariance, and causality within a single framework to enha

Cited by 13 Source ↗code ↗Scholar ↗

2023

DROID: Learning from Offline Heterogeneous Demonstrations via Reward-Policy Distillation

Sravan Jayanthi, Letian Chen, Nadya Balabanska, Van Duong, Erik Scarlatescu, Ezra Ameperosa +5

CoRL 2023poster

Offline Learning from Demonstrations (OLfD) is valuable in domains where trial-and-error learning is infeasible or specifying a cost function is difficult, such as robotic surgery, autonomous driving, and path-finding for NASA's Mars rovers. However, two key problems remain challenging in OLfD: 1) h…

Cited by 5 Source ↗Scholar ↗