Quantum Bayesian Optimization for Robotics is a method for optimizing the probability distribution of a robot's actions in a complex environment, taking into account the uncertainty of quantum mechanics and the Bayesian uncertainty of human decision-making. By applying quantum principles to traditional machine learning, we can achieve near-optimal solutions that outperform classical methods.
Here are a few examples of applications in robotics:
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