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Jonathan P. How

4 papers indexed

arxivcs.GTcs.AIcs.LGcs.MA2026-07-10

Beyond Bayesian Nash: Learning Minimax-Regret Equilibria for Adversarial Team Games under Asymmetric Information

Naman Aggarwal, Jonathan P. How

Adversarial team games (ATGs) with asymmetric information, such as adversarial path-finding, goal search, and reachability games on graphs, require strategies that are robust to hidden opponent types, such as a hidden goal flag, and to deception. Under asymmetric information, dec…

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arxivcs.RO2026-07-10

CORAL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles

Steven Roche, Milo Van Mooy, Nathan McGuire, Levi Cai, Jonathan P. How, Yogesh Girdhar

Fine grain control and positioning of autonomous underwater vehicles (AUVs) is critical for sampling, maintenance, and survey applications. Traditional control methods for AUVs are labor intensive and are not robust to changes in the vehicle configuration or environmental conditi…

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arxivcs.RO2026-07-07

CILC: Cryptographically-secure Inter-agent Loop Closure Candidate Detection for Multi-Agent Collaborative SLAM

Andrew Fishberg, Yixuan Jia, Jonathan P. How

Multi-agent Simultaneous Localization and Mapping (SLAM) and collaborative SLAM (CSLAM) require robots to continuously exchange global descriptors (GDs) to detect inter-agent loop closures (ILCs). While encrypted radios protect this traffic from external eavesdroppers, they offer…

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arxivcs.CVcs.RO2026-06-29

GaussLite: Online Task-Conditioned 3D Gaussian Splatting for Real-Time Robotic Mapping

Annika Thomas, Mason Peterson, Jonathan P. How

Existing 3D Gaussian Splatting (3DGS) systems distribute representation capacity uniformly across a scene, ignoring the fact that many downstream robotic tasks engage only a fraction of the reconstructed geometry. This causes valuable onboard compute to be allocated towards optim…

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