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…
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…
Merry-Go-Round: Safe Control of Decentralized Multi-Robot Systems with Deadlock Prevention
Wonjong Lee, Joonyeol Sim, Joonkyung Kim +3
We propose a hybrid approach for decentralized multi-robot navigation that ensures both safety and deadlock prevention. Building on a standard control formulation, we add a lightwe…