4 citations · 4 across the 10 of their papers we have counts for
10 papers
Unified Video-Action Joint Denoising for Dexterous Action and Data Generation
Dingrui Wang, YuAn Wang, Jinkun Liu +4
Recent world action models leverage video foundation models by aligning broad visual-dynamics priors with executable robot actions. We revisit this alignment from a distributional…
Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning
Roberto Brusnicki, David Pop, Yuan Gao +2
Autonomous driving systems remain critically vulnerable to the long-tail of rare, out-of-distribution semantic anomalies. While VLMs have emerged as promising tools for perception,…
NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving
Yuan Gao, Mattia Piccinini, Roberto Brusnicki +2
Understanding risk in autonomous driving requires not only perception and prediction, but also high-level reasoning about agent behavior and context. Current Vision Language Model…
Model-Structured Neural Networks to Control the Steering Dynamics of Autonomous Race Cars
Mattia Piccinini, Aniello Mungiello, Georg Jank +3
Autonomous racing has gained increasing attention in recent years, as a safe environment to accelerate the development of motion planning and control methods for autonomous driving…
MP-RBFN: Learning-based Vehicle Motion Primitives using Radial Basis Function Networks
Marc Kaufeld, Mattia Piccinini, Johannes Betz
This research introduces MP-RBFN, a novel formulation leveraging Radial Basis Function Networks for efficiently learning Motion Primitives derived from optimal control problems for…
Informed Hybrid Zonotope-based Motion Planning Algorithm
Peng Xie, Johannes Betz, Amr Alanwar
Optimal path planning in nonconvex free spaces poses substantial computational challenges. A common approach formulates such problems as mixed-integer linear programs (MILPs); howe…