3 citations · 3 across the 6 of their papers we have counts for
6 papers
Provably Safe, Yet Scalable Reinforcement Learning
Kai S. Yun, Zeyang Li, Navid Azizan
Safe reinforcement learning (RL) aims to learn policies that optimize rewards while satisfying constraints. Predominant approaches rely on soft-constrained policy optimization, whi…
ATOM-CBF: Adaptive Safe Perception-Based Control under Out-of-Distribution Measurements
Kai S. Yun, Navid Azizan
Ensuring the safety of real-world systems is challenging, especially when they rely on learned perception modules to infer the system state from high-dimensional sensor data. These…
SPARK: Safe Protective and Assistive Robot Kit
Yifan Sun, Rui Chen, Kai S. Yun +6
This paper introduces the Safe Protective and Assistive Robot Kit (SPARK), a comprehensive benchmark designed to ensure safety in humanoid autonomy and teleoperation. Humanoid robo…
Safe Control of Quadruped in Varying Dynamics via Safety Index Adaptation
Kai S. Yun, Rui Chen, Chase Dunaway +2
Varying dynamics pose a fundamental difficulty when deploying safe control laws in the real world. Safety Index Synthesis (SIS) deeply relies on the system dynamics and once the dy…
ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks
Tianhao Wei, Hanjiang Hu, Luca Marzari +4
Deep Neural Networks (DNN) are crucial in approximating nonlinear functions across diverse applications, ranging from image classification to control. Verifying specific input-outp…
Synthesis and verification of robust-adaptive safe controllers
Simin Liu, Kai S. Yun, John M. Dolan +1
Safe control with guarantees generally requires the system model to be known. It is far more challenging to handle systems with uncertain parameters. In this paper, we propose a ge…