AutoSafe is a policy architecture that integrates structured safety monitoring for continuous safe online RL on continuous-control tasks and a physical cart-pole.
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2 Pith papers cite this work, alongside 538 external citations. Polarity classification is still indexing.
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Develops and tests a model-based RL controller with post-training for gait in a tendon-driven soft quadruped, reporting improved efficiency and robustness over benchmarks.
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Safe Online Learning via Smooth Safety-Structured Policy Composition
AutoSafe is a policy architecture that integrates structured safety monitoring for continuous safe online RL on continuous-control tasks and a physical cart-pole.
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Optimal Gait Control for a Tendon-driven Soft Quadruped Robot by Model-based Reinforcement Learning
Develops and tests a model-based RL controller with post-training for gait in a tendon-driven soft quadruped, reporting improved efficiency and robustness over benchmarks.