6 citations · 11 across the 8 of their papers we have counts for
8 papers · 1 filter
World2Rules: A Neuro-Symbolic Framework for Learning World-Governing Safety Rules for Aviation
Haichuan Wang, Jay Patrikar, Sebastian Scherer
Many real-world safety-critical systems are governed by explicit rules that define unsafe world configurations and constrain agent interactions. In practice, these rules are comple…
Don't Run with Scissors: Pruning Breaks VLA Models but They Can Be Recovered
Jason Jabbour, Dong-Ki Kim, Max Smith +6
Vision-Language-Action (VLA) models have advanced robotic capabilities but remain challenging to deploy on resource-limited hardware. Pruning has enabled efficient compression of l…
The Case for Negative Data: From Crash Reports to Counterfactuals for Reasonable Driving
Jay Patrikar, Apoorva Sharma, Sushant Veer +3
Learning-based autonomous driving systems are trained mostly on incident-free data, offering little guidance near safety-performance boundaries. Real crash reports contain precisel…
AutoODD: Agentic Audits via Bayesian Red Teaming in Black-Box Models
Rebecca Martin, Jay Patrikar, Sebastian Scherer
Specialized machine learning models, regardless of architecture and training, are susceptible to failures in deployment. With their increasing use in high risk situations, the abil…
Demonstrating ViSafe: Vision-enabled Safety for High-speed Detect and Avoid
Parv Kapoor, Ian Higgins, Nikhil Keetha +9
Assured safe-separation is essential for achieving seamless high-density operation of airborne vehicles in a shared airspace. To equip resource-constrained aerial systems with this…
Challenges in Close-Proximity Safe and Seamless Operation of Manned and Unmanned Aircraft in Shared Airspace
Jay Patrikar, Joao P. A. Dantas, Sourish Ghosh +11
We propose developing an integrated system to keep autonomous unmanned aircraft safely separated and behave as expected in conjunction with manned traffic. The main goal is to achi…