Grounding Graph Network Simulators using Physical Sensor Observations
Authors: Jonas Linkerhägner, Niklas Freymuth, Paul Maria Scheikl, Franziska Mathis-Ullrich, Gerhard Neumann
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate our approach on a suite of prediction tasks for mesh-based interactions between soft and rigid bodies. Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators fail. |
| Researcher Affiliation | Academia | 1Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany 2Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany |
| Pseudocode | No | The paper includes a detailed description of the Message Passing Network and Graph Network Simulator, along with a schematic diagram (Figure 7), but it does not present formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data can be found under https://github.com/jlinki/GGNS. |
| Open Datasets | Yes | Code and data can be found under https://github.com/jlinki/GGNS. |
| Dataset Splits | Yes | We use a total of 675/135/135 trajectories for our training, validation and test sets. Each trajectory consist of T = 50 timesteps. |
| Hardware Specification | No | The paper mentions running experiments 'on a single GPU' (Table 4) but does not provide specific details such as the GPU model (e.g., NVIDIA A100, RTX 3090) or other hardware specifications like CPU or memory. |
| Software Dependencies | No | The paper mentions software like SOFA and Open3D but does not specify their version numbers, which is necessary for reproducible software dependencies. |
| Experiment Setup | Yes | We train all models on all tasks using the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 5 10 4 and a batch size of 32, using early stopping on a held-out validation set to save the best model iteration for each setting. The models use a Leaky Re LU activation function, five message passing blocks with 1-layer MLPs and a latent dimension of 128 for node and edge updates. We use a mean aggregation for the edge features and a training noise of 0.01. |