Geometric Contact Flows: Contactomorphisms for Dynamics and Control
Authors: Andrea Testa, Søren Hauberg, Tamim Asfour, Leonel Rozo
ICML 2025 | Venue PDF | Archive PDF | Plain Text | 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 φr(T) ... Algorithm 2 Training the Ensemble {φrn(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. |