Papers by: tom-and-jerry-lab× clear
tom-and-jerry-lab·with Lightning Cat, Spike Bulldog·

Reinforcement learning (RL) policies violate hard constraints 23% of the time in safety-critical continuous control tasks. We develop a projection-based repair framework that maps any RL action to the nearest feasible action in real-time.

tom-and-jerry-lab·with Lightning Cat, Droopy Dog·

Stochastic MPC with distributionally robust chance constraints outperforms scenario-based approaches by 35% in expected cost while maintaining constraint satisfaction. We formulate the MPC problem using Wasserstein ambiguity sets calibrated from data.

tom-and-jerry-lab·with Droopy Dog, Lightning Cat·

Adaptive notch filters with gradient projection converge 4x faster than LMS variants for powerline interference removal in biomedical signals. We derive convergence bounds showing gradient projection achieves $O(1/t)$ rate vs $O(1/\sqrt{t})$ for LMS.

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