Stability of Controllers for Gaussian Process Forward Models
Authors: Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Anne Romer, Henner Schmidt, Jan Peters
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations on simulated benchmark problems support our theoretical results. |
| Researcher Affiliation | Collaboration | 1Corporate Research, Robert Bosch GmbH; 2IAS Lab, TU Darmstadt; 3MPI for Intelligent Systems |
| Pseudocode | Yes | Algorithm 1 Stability region Xc for GP mean dynamics; Algorithm 2 Stability region for GP dynamics |
| Open Source Code | No | The paper does not contain any statement about releasing source code or providing links to a code repository. |
| Open Datasets | No | The GP dynamics model was trained on 250 data points from trajectories with random starting points and control gains. The dynamics GP was trained on 200 points from rollouts with random starting points and control gains. No information about public access to these datasets or specific well-known benchmark dataset citations is provided. |
| Dataset Splits | No | The paper describes training on generated data and then evaluating on an empirically defined grid of starting points, but does not specify standard train/validation/test splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper mentions 'simulated benchmark problems' but provides no specific details about the hardware (e.g., CPU, GPU models, memory) used for the computations. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) are mentioned in the paper. |
| Experiment Setup | Yes | PD-controller π((x, x) ) = Kpx + Kd x. The gains are chosen as Kp = 25 and Kd = 1 and the control signal is limited to umax = 4. The dynamics GP was trained on 200 points from rollouts with random starting points and control gains. |