Introduces a Stein variational inference-based deterministic formulation for distributionally robust control in contact-rich robotic manipulation, reporting up to 3x improved robustness under parametric uncertainty.
Sensor-based distri- butionally robust control for safe robot navigation in dy- namic environments
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 3representative citing papers
A DRL-trained unrolled QP network serves as a model-free safety filter with formal persistent safety guarantees.
Neural CDF barriers enable efficient planning and distributionally robust safe control for manipulators in cluttered dynamic environments using only point-cloud observations.
citing papers explorer
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Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation
Introduces a Stein variational inference-based deterministic formulation for distributionally robust control in contact-rich robotic manipulation, reporting up to 3x improved robustness under parametric uncertainty.
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Verifiable Model-Free Safety Filters via Reinforcement Learning
A DRL-trained unrolled QP network serves as a model-free safety filter with formal persistent safety guarantees.
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Neural Configuration-Space Barriers for Manipulation Planning and Control
Neural CDF barriers enable efficient planning and distributionally robust safe control for manipulators in cluttered dynamic environments using only point-cloud observations.