Daniel Butterfield — accepted AI-conference papers

2025

MI-HGNN: Morphology-Informed Heterogeneous Graph Neural Network for Legged Robot Contact Perception

Daniel Butterfield, Sandilya Sai Garimella, Nai-Jen Cheng, Lu Gan

ICRA 2025

We present a Morphology-Informed Heterogeneous Graph Neural Network (MI-HGNN) for learning-based contact perception. The architecture and connectivity of the MI-HGNN are constructed from the robot morphology, in which nodes and edges are robot joints and links, respectively. By incorporating the mor

Cited by 9 Source ↗code ↗Scholar ↗

2023

AcTag: Opti-Acoustic Fiducial Markers for Underwater Localization and Mapping

Kalin Norman, Daniel Butterfield, Joshua G. Mangelson

IROS 2023poster

Fiducial markers are important tools for robotic navigation and imaging, enabling accurate localization and tracking of objects in challenging environments. In this paper, we present AcTag, a new fiducial marker design for use underwater with imaging sonar and cameras, as well as a method for the de…

Cited by 4 Source ↗Scholar ↗