1 code implementation • 3 Aug 2024 • Fengjun Yang, Nikolai Matni
We propose a reinforcement learning (RL)-based algorithm to jointly train (1) a trajectory planner and (2) a tracking controller in a layered control architecture.
1 code implementation • 28 Oct 2022 • Fengjun Yang, Fernando Gama, Somayeh Sojoudi, Nikolai Matni
Designing distributed optimal controllers subject to communication constraints is a difficult problem unless structural assumptions are imposed on the underlying dynamics and information exchange structure, e. g., sparsity, delay, or spatial invariance.
1 code implementation • 8 Jun 2022 • Haoze Wu, Teruhiro Tagomori, Alexander Robey, Fengjun Yang, Nikolai Matni, George Pappas, Hamed Hassani, Corina Pasareanu, Clark Barrett
We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts.
2 code implementations • 22 Dec 2021 • Carmen Amo Alonso, Fengjun Yang, Nikolai Matni
By imposing locality constraints on the system response, we show that the amount of data needed for our synthesis problem is independent of the size of the global system.
1 code implementation • 28 Apr 2021 • Fengjun Yang, Nikolai Matni
Our proposed parameterization enjoys a local and distributed architecture, similar to previous Graph Neural Network (GNN)-based parameterizations, while further naturally allowing for joint optimization of the distributed controller and communication topology needed to implement it.