7 papers
Learning Contact Dynamics through Touching: Action-conditional Graph Neural Networks for Robotic Peg Insertion
Zongyao Yi, Joachim Hertzberg, Martin Atzmueller
We present a learnable physics-based predictive model that provides accurate motion and force-torque prediction of the robot end effector in contact-rich manipulation. The proposed…
Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?
Viacheslav Barkov, Jonas Schmidinger, Robin Gebbers +1
In the field of pedometrics, tabular machine learning is the predominant method for soil property prediction from remote and proximal soil sensing data, forming a central component…
Kriging prior Regression: A Case for Kriging-Based Spatial Features with TabPFN in Soil Mapping
Jonas Schmidinger, Viacheslav Barkov, Sebastian Vogel +2
Machine learning and geostatistics are two fundamentally different frameworks for predicting and spatially mapping soil properties. Geostatistics leverages the spatial structure of…
Comprehensive Evaluation of Prototype Neural Networks
Philipp Schlinge, Steffen Meinert, Martin Atzmueller
Prototype models are an important method for explainable artificial intelligence (XAI) and interpretable machine learning. In this paper, we perform an in-depth analysis of a set o…
Integrating Prior Observations for Incremental 3D Scene Graph Prediction
Marian Renz, Felix Igelbrink, Martin Atzmueller
3D semantic scene graphs (3DSSG) provide compact structured representations of environments by explicitly modeling objects, attributes, and relationships. While 3DSSGs have shown p…
Uncertainty-Resilient Active Intention Recognition for Robotic Assistants
Juan Carlos SaborÃo, Marc Vinci, Oscar Lima +6
Purposeful behavior in robotic assistants requires the integration of multiple components and technological advances. Often, the problem is reduced to recognizing explicit prompts,…