From the 1 of 7 linked papers with an AI index.
7 papers
Grow-Prune-Freeze Networks: Adaptive & Continual Learning Technique for Olfactory Navigation
Kordel K. France, Ovidiu Daescu
The paper proposes Grow‑Prune‑Freeze (GPF) networks, an adaptive continual‑learning framework that dynamically grows, prunes, and freezes early policy layers to enable robots to na…
Chasing Ghosts: A Simulation-to-Real Olfactory Navigation Stack with Optional Vision Augmentation
Kordel K. France, Ovidiu Daescu, Latifur Khan +1
Autonomous odor source localization remains a challenging problem for aerial robots due to turbulent airflow, sparse and delayed sensory signals, and strict payload and compute con…
BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learning
Yi Yang, Ovidiu Daescu
Fragment-level representations provide a natural way to capture recurring molecular substructures and reuse their learned representations across molecules. However, a shared fragme…
Diffusion Graph Neural Networks and Dataset for Robust Olfactory Navigation in Hazard Robotics
Kordel K. France, Ovidiu Daescu
Navigation by scent is a capability in robotic systems that is rising in demand. However, current methods often suffer from ambiguities, particularly when robots misattribute odour…
Olfactory Inertial Odometry: Sensor Calibration and Drift Compensation
Kordel K. France, Ovidiu Daescu, Anirban Paul +1
Visual inertial odometry (VIO) is a process for fusing visual and kinematic data to understand a machine's state in a navigation task. Olfactory inertial odometry (OIO) is an analo…
Olfactory Inertial Odometry: Methodology for Effective Robot Navigation by Scent
Kordel K. France, Ovidiu Daescu
Olfactory navigation is one of the most primitive mechanisms of exploration used by organisms. Navigation by machine olfaction (artificial smell) is a very difficult task to both s…