8 papers
ForVis: An In-Field Dataset and Benchmark for VIO Using Under-Canopy UAV Flights in Forests
Arman Kiani, Masoud Ataei, Elvis Gyaase +4
Visual-inertial Simultaneous Localization and Mapping (VI-SLAM) for UAVs remains difficult to evaluate in real forest environments, where motion, illumination changes, repetitive v…
SE(3) Neural Potential Fields for 6-DoF Trajectory Planning Directly from Images Without Explicit 3D Reconstruction
Jeffrey Eiyike, Masoud Ataei, Elvis Gyaase +1
Reaching a 6-DoF grasp pose in clutter requires a collision-free trajectory, conventionally obtained by reconstructing the scene in 3D and planning inside that reconstruction, at t…
Draining Fictitious Knots: Restoring Distance-Awareness Guarantees for High-Dimensional Spline Networks
Masoud Ataei, Mohammad Javad Khojasteh, Vikas Dhiman
Kolmogorov-Arnold Networks (KANs) with spline activations have recently shown promise for interpretable function approximation. Distance-Aware Error for Kolmogorov Networks (DAREK)…
Engagement-Aware Agentic Pursuit-Evasion
Ananya Acharya, Trenton Goyette, Masoud Ataei +3
This paper presents a hierarchical multi-agent architecture in which independent large language model (LLM) planners perform strategic role assignment for attacking and defending r…
Worst-Case Distance-Aware Error Bounds for Neural Networks
Masoud Ataei, Vikas Dhiman, Mohammad Javad Khojasteh
Safety-critical applications of machine learning require uncertainty estimates that support reliable worst-case analysis. Neural networks (NNs) provide expressive function approxim…
Distance-Aware Error for Spline Networks: A Bottom-Up Approach to Uncertainty
Masoud Ataei, Mohammad Javad Khojasteh, Vikas Dhiman
We develop a new class of distance-aware error bounds that tightly characterize the approximation error of spline neural networks. Our bottom-up approach analyzes the error bound o…
Omobot: a low-cost mobile robot for autonomous search and fall detection
Shihab Uddin Ahamad, Masoud Ataei, Vijay Devabhaktuni +1
Detecting falls among the elderly and alerting their community responders can save countless lives. We design and develop a low-cost mobile robot that periodically searches the hou…
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety
Masoud Ataei, Vikas Dhiman
Uncertainty-aware controllers that guarantee safety are critical for safety critical applications. Among such controllers, Control Barrier Functions (CBFs) based approaches are pop…