Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach
Large-scale cyber-physical systems require that control policies are distributed, that is, that
they only rely on local real-time measurements and communication with neighboring agents …
they only rely on local real-time measurements and communication with neighboring agents …
Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Towards Trustworthy, Interpretable, and Explainable …
This article presents a literature review of the past five years of studies using Deep
Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic …
Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic …
Learning over all stabilizing nonlinear controllers for a partially-observed linear system
This letter proposes a nonlinear policy architecture for control of partially-observed linear
dynamical systems providing built-in closed-loop stability guarantees. The policy is based …
dynamical systems providing built-in closed-loop stability guarantees. The policy is based …
Goal-conditioned reinforcement learning within a human-robot disassembly environment
Í Elguea-Aguinaco, A Serrano-Muñoz… - Applied Sciences, 2022 - mdpi.com
The introduction of collaborative robots in industrial environments reinforces the need to
provide these robots with better cognition to accomplish their tasks while fostering worker …
provide these robots with better cognition to accomplish their tasks while fostering worker …
A reinforcement learning-based control strategy for robust interaction of robotic systems with uncertain environments
In the context of interaction with unmodelled systems, it becomes imperative for a robot
controller to possess the capability to dynamically adjust its actions in real-time, enhancing …
controller to possess the capability to dynamically adjust its actions in real-time, enhancing …
Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach
M Zakwan, G Ferrari-Trecate - arXiv preprint arXiv:2411.10096, 2024 - arxiv.org
The control of large-scale cyber-physical systems requires optimal distributed policies
relying solely on limited communication with neighboring agents. However, computing …
relying solely on limited communication with neighboring agents. However, computing …
Neural Distributed Controllers with Port-Hamiltonian Structures
M Zakwan, G Ferrari-Trecate - arXiv preprint arXiv:2403.17785, 2024 - arxiv.org
Controlling large-scale cyber-physical systems necessitates optimal distributed policies,
relying solely on local real-time data and limited communication with neighboring agents …
relying solely on local real-time data and limited communication with neighboring agents …
Closed‐loop stability analysis of deep reinforcement learning controlled systems with experimental validation
Trained deep reinforcement learning (DRL) based controllers can effectively control
dynamic systems where classical controllers can be ineffective and difficult to tune …
dynamic systems where classical controllers can be ineffective and difficult to tune …
Tactile-Morph Skills: Energy-Based Control Meets Data-Driven Learning
Robotic manipulation is essential for modernizing factories and automating industrial tasks
like polishing, which require advanced tactile abilities. These robots must be easily set up …
like polishing, which require advanced tactile abilities. These robots must be easily set up …
Embedding koopman optimal control in robot policy learning
Embedding an optimization process has been explored for imposing efficient and flexible
policy structures. Existing work often build upon nonlinear optimization with explicitly …
policy structures. Existing work often build upon nonlinear optimization with explicitly …