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Physics-informed neural networks, combined with formal verification, can now provide certifiable estimates of safe and robust domains of attraction for complex, uncertain dynamical systems.
LLMs struggle to explore multiple valid reasoning paths, often committing to a single route and missing alternative solutions, especially in complex, multi-step logical problems.
Control hybrid rigid-soft robots with the ease of AR teleoperation, thanks to a new pipeline that accurately models the soft robot's real-world behavior in simulation.
