Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Meta-Adaptive Nonlinear Control: Theory and Algorithms
Authors: Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell, Soon-Jo Chung, Yisong Yue
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that OMAC significantly outperforms conventional adaptive control approaches which do not learn the shared representation, in inverted pendulum and 6-Do F drone control tasks under varying wind conditions1. |
| Researcher Affiliation | Academia | Caltech Purdue University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Online Meta-Adaptive Control (OMAC) algorithm |
| Open Source Code | Yes | Code and video: https://github.com/GuanyaShi/Online-Meta-Adaptive-Control |
| Open Datasets | No | The paper describes experiments on simulated control tasks (inverted pendulum and 6-Do F quadrotor) using models, rather than relying on or providing access to publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper defines outer and inner iterations (N environments, T time steps) and uses Average Control Error (ACE) as a performance metric, but does not provide explicit train/validation/test dataset splits as it relies on simulated control environments. |
| Hardware Specification | No | The paper describes the experimental setup in terms of tasks (inverted pendulum, drone control) and controllers, but does not specify the hardware used to run these experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper mentions algorithms and optimizers used (e.g., Adam optimizer, spectral normalization) but does not provide specific software dependencies with version numbers (e.g., 'PyTorch 1.9' or 'Python 3.8'). |
| Experiment Setup | Yes | For all methods, we randomly switch the environment (wind) c every 2 s. To make a fair comparison, except no-adapt or omniscient, all methods have the same learning rate for the inner-adapter A2 and the dimensions of ˆc are also same (dim(ˆc) = 20 for the pendulum and dim(ˆc) = 30 for the drone). |