5 papers
Safety-Critical Control under Uncertainty via Adaptive Conformal Quantile Prediction Intervals
Hao Zhou, Yanze Zhang, Yiwei Lyu +1
Safety-critical control under uncertainty requires uncertainty representations that are both statistically valid (for certifiable performance) and compatible with enforceable safet…
Certifiable Safe Model-Based Reinforcement Learning with Control-Affine Dynamics Approximation
Hao Zhou, Yanze Zhang, Cameron Reid +1
Safe model-based reinforcement learning (RL) often bridges control-theoretic analysis and RL for robots to safely explore (partially) unknown system dynamics while deriving control…
Privacy-Preserving Distributed Stochastic Optimization with Homomorphic Encryption and Heterogeneous Stepsizes
Haoqiang Zhou, Chi Chen, Yongfeng Zhi +1
Distributed stochastic optimization enables multi-agent collaboration in applications such as distributed learning and sensor networks, but also raises critical privacy concerns du…
Computationally and Sample Efficient Safe Reinforcement Learning Using Adaptive Conformal Prediction
Hao Zhou, Yanze Zhang, Wenhao Luo
Safety is a critical concern in learning-enabled autonomous systems especially when deploying these systems in real-world scenarios. An important challenge is accurately quantifyin…
Safety-Critical Control with Uncertainty Quantification using Adaptive Conformal Prediction
Hao Zhou, Yanze Zhang, Wenhao Luo
Safety assurance is critical in the planning and control of robotic systems. For robots operating in the real world, the safety-critical design often needs to explicitly address un…