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Found 5 papers, 2 papers with code
Large Language Models to the Rescue: Deadlock Resolution in Multi-Robot Systems
Multi-agent robotic systems are prone to deadlocks in an obstacle environment where the system can get stuck away from its desired location under a smooth low-level control policy.
Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent Control
We consider the problem of designing distributed collision-avoidance multi-agent control in large-scale environments with potentially moving obstacles, where a large number of agents are required to maintain safety using only local information and reach their goals.
Learning to Stabilize High-dimensional Unknown Systems Using Lyapunov-guided Exploration
LYGE employs Lyapunov theory to iteratively guide the search for samples during exploration while simultaneously learning the local system dynamics, control policy, and Lyapunov functions.
Compositional Neural Certificates for Networked Dynamical Systems
Specifically, we treat a large networked dynamical system as an interconnection of smaller subsystems and develop methods that can find each subsystem a decentralized controller and an ISS Lyapunov function; the latter can be collectively composed to prove the global stability of the system.
Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality
Our results show that CAIL significantly outperforms other imitation learning methods from demonstrations with varying optimality.
