Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions
Stability guarantees are crucial when ensuring that a fully autonomous robot does not take undesirable or potentially harmful actions. We recently proposed the Neural Contractive Dynamical Systems (NCDS), which is a neural network architecture that guarantees contractive stability. With this, learni...
Gespeichert in:
Hauptverfasser: | , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Stability guarantees are crucial when ensuring that a fully autonomous robot
does not take undesirable or potentially harmful actions. We recently proposed
the Neural Contractive Dynamical Systems (NCDS), which is a neural network
architecture that guarantees contractive stability. With this,
learning-from-demonstrations approaches can trivially provide stability
guarantees. However, our early work left several unanswered questions, which we
here address. Beyond providing an in-depth explanation of NCDS, this paper
extends the framework with more careful regularization, a conditional variant
of the framework for handling multiple tasks, and an uncertainty-driven
approach to latent obstacle avoidance. Experiments verify that the developed
system has the flexibility of ordinary neural networks while providing the
stability guarantees needed for autonomous robotics. |
---|---|
DOI: | 10.48550/arxiv.2411.11405 |