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Humanoid robots can now nimbly manipulate objects across much larger workspaces thanks to a LiDAR-powered perception system that eliminates the need for constant repositioning.
A practical VLA model, LLaVA-VLA, achieves strong generalization and versatility on a new benchmark, CEBench, while running on consumer-grade GPUs, eliminating the need for costly pre-training.
By "dreaming" plausible scene completions, Dream-SLAM enables robots to navigate dynamic environments more effectively, achieving better localization, mapping, and exploration than existing methods.
By aligning latent representations with multiple visual foundation models, FRAPPE offers a more scalable and data-efficient way to imbue generalist robotic policies with robust world-awareness.
Certifiably optimal solutions to 3D vision problems are now within reach, but choosing the right global solver (BnB, CR, or GNC) requires navigating a complex trade-off between optimality, robustness, and scalability.
