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Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025
This paper presents SparseLoc, a global localization system that uses vision-language models to build sparse, semantic maps. It achieves localization accuracy comparable to dense LiDAR methods while using 1/500th of the points.
Published in International Conference on Learning Representations (ICLR), 2025
This paper proposes a new framework for evaluating LLM-based forecasters by measuring their logical consistency, introducing an “arbitrage metric” that correlates strongly with future forecasting accuracy, even when the ground truth is unknown.
Published in Conference on Robot Learning (CoRL), 2025
The paper introduces an object-relative control framework for visual navigation using a relative 3D scene graph, enabling more flexible, embodiment-invariant path planning and control that generalizes better across tasks and real-world environments than traditional image-relative methods.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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