Online Learning and Control of Complex Dynamical Systems from Sensory Input
Authors: Oumayma Bounou, Jean Ponce, Justin Carpentier
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed approach is evaluated empirically on several classical dynamical systems and sensory modalities, with good performance on long-term prediction and control. |
| Researcher Affiliation | Academia | 1Inria and Département d Informatique de l Ecole Normale Supérieure, PSL Research University 2Center for Data Science, New York University |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | 3. (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] see supplemental material |
| Open Datasets | No | We generate three black-and-white 64 64 video datasets of classical dynamical systems using the Pinocchio library [49] for the simulation and Panda3D-viewer [50] for the rendering: a simple pendulum, a double pendulum1 and a cartpole. ... More details on the datasets generation can be found in the supplementary material. |
| Dataset Splits | No | The paper mentions "training datasets" and evaluation on sequences of different lengths (e.g., "trained on 5 s sequences" and "evaluated on 15 s sequences") but does not explicitly specify train/validation/test splits or mention a "validation" set. |
| Hardware Specification | Yes | Each learning problem takes about 3 hours to solve on an Nvidia RTX6000 GPU. |
| Software Dependencies | No | The paper mentions software like "Pinocchio library [49]", "Panda3D-viewer [50]", and "Py Torch" but does not specify their version numbers. |
| Experiment Setup | Yes | The dimension of the codes in latent space corresponds to the bottleneck size of the autoencoder, set to nz = 8 in all our experiments. More details on the architecture can be found in the supplementary material. The models of the unactuated systems are trained on 5 s sequences and evaluated on 15 s sequences, and those of actuated systems were trained on 10 s sequences and evaluated on 20 s sequences. |