Concetti Chiave
Topological tools offer data-efficient RoA estimation for high-dimensional robot controllers.
Sintesi
The paper introduces MORALS, a method that combines auto-encoding neural networks with Morse Graphs to estimate Regions of Attraction (RoAs) in a learned Latent Space. By projecting the dynamics of controlled systems into a lower-dimensional latent space, MORALS efficiently identifies attractors and their RoAs for data-driven controllers operating over high-dimensional systems. The approach involves constructing Morse Graphs to represent the bistability of controller dynamics, distinguishing between desired and undesired behaviors. Through experimental evaluation on various robotic datasets, MORALS demonstrates data efficiency in RoA estimation without requiring an analytical model.
Statistiche
"67-dim humanoid robot"
"96-dim 3-fingered manipulator"
Citazioni
"Estimating the region of attraction (RoA) for a robot controller is essential for safe application and controller composition."
"MORALS shows promising predictive capabilities in estimating attractors and their RoAs for data-driven controllers operating over high-dimensional systems."