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..
Geometric Contact Flows: Contactomorphisms for Dynamics and Control
Authors: Andrea Testa, Sรธren Hauberg, Tamim Asfour, Leonel Rozo
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on learning dynamics for physical systems and for controlling robots on interaction tasks demonstrate the effectiveness of our approach. ... 6. Experiments |
| Researcher Affiliation | Collaboration | 1Bosch Center for Artificial Intelligence, Renningen, Germany 2Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany 3Section of Cognitive Systems, Technical University of Denmark (DTU), Lyngby, Denmark. |
| Pseudocode | Yes | Algorithm 1 Training a Contactomorphism ฯr(T) ... Algorithm 2 Training the Ensemble {ฯrn(T)}n [1,N] |
| Open Source Code | Yes | A full video of the robot experiments, including adaptability to unseen obstacles, and the repository implementing the GCF framework are available at https://sites.google. com/view/geometric-contact-flows. |
| Open Datasets | Yes | We consider a 60-dimensional dataset (Otness et al., 2021) describing the dynamics of a 2D square grid of nodes connected by springs. ... handwriting dynamics reconstruction using two datasets (Lemme et al., 2015; Fabi et al., 2022) |
| Dataset Splits | No | The training dataset comprises 20 spring-mesh systems, each characterized by a distinct set of initial conditions. While training data is mentioned, explicit splits for training/validation/testing with percentages or sample counts are not provided for any dataset. |
| Hardware Specification | Yes | The framework runs on a machine equipped with 13th Gen Intel Core i7-13850HX CPUs. |
| Software Dependencies | No | The paper mentions 'ROS2 acting as the middleware' but does not specify a version number. No other specific software components with version numbers are provided. |
| Experiment Setup | Yes | The weights employed in the loss function (12) are wx = 1 and wz = 0.01. Training is conducted for 5000 epochs, taking approximately 4 hours on average. ... The initial learning rate is 1 10 3 and is reduced by a factor of 0.9 on plateaus observed for 200 epochs. The loss is clipped at 1 103, and the gradient is clipped at 0.1. Optimization is performed using the Adam optimizer with default hyperparameters, and L2 regularization is applied to the weights with a coefficient of 1 10 10. |