11 papers · 1 filter
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…
Spatio-Temporal Reconnection for Multi-Robot Networks using Adaptive Prescribed-Time CBFs
Hao Liu, Yupeng Yang, Yanze Zhang +1
In multi-robot systems, maintaining persistent communication graph connectivity is often overly restrictive, especially when robots have limited communication ranges but operate in…
Geometry-Aware Control Barrier Functions for Collision Avoidance via Bernstein Polynomial Approximations
Siwon Jo, Yanze Zhang, Yupeng Yang +1
Safe navigation often relies on well-defined conditions based on the shape of robots and obstacles, and can be challenging when they have irregular geometries. While Control Barrie…
Capability-Aware Heterogeneous Control Barrier Functions for Decentralized Multi-Robot Safe Navigation
Joonkyung Kim, Yanze Zhang, Wenhao Luo +1
Safe navigation for multi-robot systems requires enforcing safety without sacrificing task efficiency under decentralized decision-making. Existing decentralized methods often assu…
Courteous MPC for Autonomous Driving with CBF-inspired Risk Assessment
Yanze Zhang, Yiwei Lyu, Sude E. Demir +4
With more autonomous vehicles (AVs) sharing roadways with human-driven vehicles (HVs), ensuring safe and courteous maneuvers that respect HVs' behavior becomes increasingly importa…
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…