6 papers
CodeGraphVLP: Code-as-Planner Meets Semantic-Graph State for Non-Markovian Vision-Language-Action Models
Khoa Vo, Sieu Tran, Taisei Hanyu +8
Vision-Language-Action (VLA) models promise generalist robot manipulation, but are typically trained and deployed as short-horizon policies that assume the latest observation is su…
From Refusal to Recovery: A Control-Theoretic Approach to Generative AI Guardrails
Ravi Pandya, Madison Bland, Duy P. Nguyen +3
Generative AI systems are increasingly assisting and acting on behalf of end users in practical settings, from digital shopping assistants to next-generation autonomous cars. In th…
Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning
Donggeon David Oh, Duy P. Nguyen, Haimin Hu +1
Robust control barrier functions (CBFs) provide a principled mechanism for smooth safety enforcement under worst-case disturbances. However, existing approaches typically rely on e…
Provably Optimal Reinforcement Learning under Safety Filtering
Donggeon David Oh, Duy P. Nguyen, Haimin Hu +1
Recent advances in reinforcement learning (RL) enable its use on increasingly complex tasks, but the lack of formal safety guarantees still limits its application in safety-critica…
MAGICS: Adversarial RL with Minimax Actors Guided by Implicit Critic Stackelberg for Convergent Neural Synthesis of Robot Safety
Justin Wang, Haimin Hu, Duy Phuong Nguyen +1
While robust optimal control theory provides a rigorous framework to compute robot control policies that are provably safe, it struggles to scale to high-dimensional problems, lead…
Gameplay Filters: Robust Zero-Shot Safety through Adversarial Imagination
Duy P. Nguyen, Kai-Chieh Hsu, Wenhao Yu +2
Despite the impressive recent advances in learning-based robot control, ensuring robustness to out-of-distribution conditions remains an open challenge. Safety filters can, in prin…