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 [1].
Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network
Authors: Ravinder Bhattoo, Sayan Ranu, N M Anoop Krishnan
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the ability of LGNN to learn rigid body dynamics. In addition, we evaluate the ability of LGNN to generalize to larger unseen system sizes, complex topology, and realistic structures such as tensegrity. |
| Researcher Affiliation | Academia | Ravinder Bhattoo Department of Civil Engineering Indian Institute of Technology Delhi EMAIL Sayan Ranu Department of Computer Science and Engineering Yardi School of Arti cial Intelligence Indian Institute of Technology Delhi EMAIL N. M. Anoop Krishnan Department of Civil Engineering Yardi School of Arti cial Intelligence Indian Institute of Technology Delhi EMAIL |
| Pseudocode | No | The paper describes the model architecture and equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/M3RG-IITD/rigid_body_dynamics_graph |
| Open Datasets | No | The paper generates its own dataset for experiments and does not explicitly state that this generated dataset is publicly available or open for access. While the code to generate the data is available, the dataset itself is not provided with concrete access information as a separate entity. |
| Dataset Splits | Yes | This dataset is divided randomly in 75:25 ratio as training and validation set. |
| Hardware Specification | Yes | Hardware: Memory: 16Gi B System memory, Processor: Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz |
| Software Dependencies | Yes | Software packages: numpy-1.20.3, jax-0.2.24, jax-md-0.1.20, jaxlib-0.1.73, jraph-0.0.1.dev0 |
| Experiment Setup | Yes | For the graph architectures, namely, LGNN and GNS, all the neural networks are modeled as one hidden layer MLPs with varying number of hidden units. For all the MLPs, a square-plus activation function is used due to its double differentiability. In contrast to the earlier approaches, here, the training is not performed on trajectories. Rather, it is performed on 10000 data points generated from 100 trajectories for all the models. This dataset is divided randomly in 75:25 ratio as training and validation set. The model performance is evaluated on a forward trajectory, a task it was not explicitly trained for, of 1s. |