5 papers
See Less, Drive Better: Generalizable End-to-End Autonomous Driving via Foundation Models Stochastic Patch Selection
Amir Mallak, Erfan Aasi, Shiva Sreeram +3
Recent advances in end-to-end autonomous driving show that policies trained on patch-aligned features extracted from foundation models generalize better to Out-of-Distribution (OOD…
ReGen: Generative Robot Simulation via Inverse Design
Phat Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong +5
Simulation plays a key role in scaling robot learning and validating policies, but constructing simulations remains a labor-intensive process. This paper introduces ReGen, a genera…
SAFe-Copilot: Unified Shared Autonomy Framework
Phat Nguyen, Erfan Aasi, Shiva Sreeram +4
Autonomous driving systems remain brittle in rare, ambiguous, and out-of-distribution scenarios, where human driver succeed through contextual reasoning. Shared autonomy has emerge…
Interpretable Imitation Learning via Generative Adversarial STL Inference and Control
Wenliang Liu, Danyang Li, Erfan Aasi +3
Imitation learning methods have demonstrated considerable success in teaching autonomous systems complex tasks through expert demonstrations. However, a limitation of these methods…
Generating Out-Of-Distribution Scenarios Using Language Models
Erfan Aasi, Phat Nguyen, Shiva Sreeram +3
The deployment of autonomous vehicles controlled by machine learning techniques requires extensive testing in diverse real-world environments, robust handling of edge cases and out…