Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915489537589248 |
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| author | Mohammadi, Hadi Beik Hauberg, Søren Arvanitidis, Georgios Neumann, Gerhard Rozo, Leonel |
| author_facet | Mohammadi, Hadi Beik Hauberg, Søren Arvanitidis, Georgios Neumann, Gerhard Rozo, Leonel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11405 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions Mohammadi, Hadi Beik Hauberg, Søren Arvanitidis, Georgios Neumann, Gerhard Rozo, Leonel Robotics Machine Learning 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. |
| title | Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2411.11405 |