A new optimistic lower-confidence-bound method for gray-box optimization that improves regret bounds for linear stochastic bandits via a recent multi-output least-squares confidence set result.
IEEE Control Systems Magazine 26(3): 96--114
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Integrates iterative learning control with a torque library to enable high-precision adaptive locomotion on bipedal and quadrupedal robots, reducing tracking errors by up to 85% and achieving over 30x faster control rates.
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Gray-Box Optimization using Optimism in the Face of Uncertainty
A new optimistic lower-confidence-bound method for gray-box optimization that improves regret bounds for linear stochastic bandits via a recent multi-output least-squares confidence set result.
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Iteratively Learning Muscle Memory for Legged Robots to Master Adaptive and High Precision Locomotion
Integrates iterative learning control with a torque library to enable high-precision adaptive locomotion on bipedal and quadrupedal robots, reducing tracking errors by up to 85% and achieving over 30x faster control rates.