Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions

• URL: http://arxiv.org/abs/2411.11405v2
• Date: Wed, 20 Nov 2024 08:20:35 GMT
• Title: Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions
• Authors: Hadi Beik Mohammadi, Søren Hauberg, Georgios Arvanitidis, Gerhard Neumann, Leonel Rozo,
• Abstract summary: We recently proposed the Neural Contractive Dynamical Systems (NCDS), which is a neural network architecture that guarantees stability. 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.
• Score: 22.80115774627255
• License:
• Abstract: 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.

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