Neural Lyapunov Control
Authors: Ya-Chien Chang, Nima Roohi, Sicun Gao
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experiments on how the new methods obtain high-quality solutions for challenging robot control problems such as path tracking for wheeled vehicles and humanoid robot balancing. We experimented with several challenging nonlinear control problems in robotics, such as drone landing, wheeled vehicle path following, and humanoid robot balancing. |
| Researcher Affiliation | Academia | Ya-Chien Chang UCSD yac021@eng.ucsd.edu Nima Roohi UCSD nroohi@eng.ucsd.edu Sicun Gao UCSD sicung@eng.ucsd.edu |
| Pseudocode | Yes | We provide pseudocode of the algorithm in Algorithm 1. |
| Open Source Code | Yes | Ya-Chien Chang, Nima Roohi, and Sicun Gao. Neural Lyapunov control (project website), https://yachienchang.github.io/Neur IPS2019. |
| Open Datasets | No | The paper uses dynamical systems as its problem domain (e.g., inverted pendulum, Caltech ducted fan), rather than traditional datasets. It samples states from the system's state space for learning, but does not provide access to a specific, pre-collected, publicly available dataset. |
| Dataset Splits | No | The paper describes a learning framework that generates samples from the state space and uses a falsifier to find counterexamples, but it does not specify explicit training, validation, or test dataset splits in terms of percentages or counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like 'SMT solvers such as d Real' and uses 'stochastic gradient descent' and 'tanh activation functions' for neural networks, but it does not provide specific version numbers for any of these software components or libraries. |
| Experiment Setup | Yes | In all the examples, we use a learning rate of 0.01 for the learner, an ε value of 0.25 and δ value of 0.01 for the falsifier, and re-verify the result with smaller ε in Table 1. |