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Enforcing state-wise safety constraints is critical for the application of reinforcement learning (RL) in real-world problems, such as autonomous driving and robot manipulation.
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Contact-rich trajectory generation in confined environments using iterative convex optimization
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Jaime F Fisac, Anayo K Akametalu, Melanie N Zeilinger, Shahab Kaynama, Jeremy Gillula, and Claire J Tomlin · 2018
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Value constrained model-free continuous control
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End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks
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Safe learning in robotics: From learning-based control to safe reinforcement learning
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Experimental evaluation of human motion prediction toward safe and efficient human robot collaboration
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Persistently feasible robust safe control by safety index synthesis and convex semi-infinite programming
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Learning predictive safety filter via decomposition of robust invariant set
Zeyang Li, Chuxiong Hu, Weiye Zhao, and Changliu Liu · 2023
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Safety index synthesis with state-dependent control space
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Tianhao Wei, Liqian Ma, Rui Chen, Weiye Zhao, and Changliu Liu · 2024
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