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The University of Texas at Austin
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Conditional branching in navigation tasks reveals hidden failures in agent decision-making that standard metrics overlook.
Achieving robust 3D spatial reasoning without any training, ViewMind3D redefines the landscape of 3D question answering.
Closing the capability gap is essential for realizing a future where drones revolutionize logistics and emergency response on a national scale.
Captions selected with VEGAS align significantly better with human attention, boosting retrieval performance and challenging the status quo of video captioning metrics.
Robots can now anticipate and adapt to environmental changes, revealing route failures that traditional planning methods miss.
A4D redefines robot interaction by enabling planning based on what objects can do, not just how they look, achieving unprecedented accuracy and speed in novel scenarios.
Forget adversarial training: a closed-form solution can make multi-agent RL for drone collision avoidance surprisingly robust to GPS spoofing.
