32 citations · 41 across the 11 of their papers we have counts for
13 papers
Mind the Temporal Gap: Evaluating Video Models for Temporal Understanding in Driving
Shiva Sreeram, Erfan Aasi, Tsun-Hsuan Wang +2
Modern video-language models are increasingly treated as general-purpose video reasoners, yet it remains unclear whether they robustly capture temporal dynamics in safety-critical…
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…
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…
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…
Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications
Mingyu Cai, Erfan Aasi, Calin Belta +1
Model-free continuous control for robot navigation tasks using Deep Reinforcement Learning (DRL) that relies on noisy policies for exploration is sensitive to the density of reward…
Time-Incremental Learning from Data Using Temporal Logics
Erfan Aasi, Mingyu Cai, Cristian Ioan Vasile +1
Real-time and human-interpretable decision-making in cyber-physical systems is a significant but challenging task, which usually requires predictions of possible future events from…
Learning Spatio-Temporal Specifications for Dynamical Systems
Suhail Alsalehi, Erfan Aasi, Ron Weiss +1
Learning dynamical systems properties from data provides important insights that help us understand such systems and mitigate undesired outcomes. In this work, we propose a framewo…
Classification of Time-Series Data Using Boosted Decision Trees
Erfan Aasi, Cristian Ioan Vasile, Mahroo Bahreinian +1
Time-series data classification is central to the analysis and control of autonomous systems, such as robots and self-driving cars. Temporal logic-based learning algorithms have be…
Inferring Temporal Logic Properties from Data using Boosted Decision Trees
Erfan Aasi, Cristian Ioan Vasile, Mahroo Bahreinian +1
Many autonomous systems, such as robots and self-driving cars, involve real-time decision making in complex environments, and require prediction of future outcomes from limited dat…
A Control Architecture for Provably-Correct Autonomous Driving
Erfan Aasi, Cristian Ioan Vasile, Calin Belta
This paper presents a novel two-level control architecture for a fully autonomous vehicle in a deterministic environment, which can handle traffic rules as specifications and low-l…
Robust Planning and Control For Polygonal Environments via Linear Programming
Mahroo Bahreinian, Erfan Aasi, Roberto Tron
We propose a novel approach for navigating in polygonal environments by synthesizing controllers that take as input relative displacement measurements with respect to a set of land…