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Total Size:
8.8 MB
Info Hash:
5B687D5B13E94E01DB60F3CBF3C76BAAE2A89E4E
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Added:
Dec. 1, 2025, 3:15 a.m.
Stats:
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(Last updated: Dec. 1, 2025, 3:16 a.m.)
| File | Size |
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| Li C. Assuring Safe Operation of Robotic Systems under Uncertainty...2025.pdf | 8.8 MB |
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| Uploaded by indexFroggy | Size 13.3 MB | Health [ 31 /4 ] | Added 2023-07-01 |
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NOTE
SOURCE: Li C. Assuring Safe Operation of Robotic Systems under Uncertainty...2025
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COVER

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MEDIAINFO
Textbook in PDF format
Assuring Safe Operation of Robotic Systems under Uncertainty: Control and Learning Methods applies set-theoretic and reinforcement learning approaches to formulate, analyze, and solve the challenge of ensuring safe operation of robotic systems in an uncertain environment.
The authors adopt learning-supported, set-theoretic methods—specifically, the barrier Lyapunov function and the control barrier function—to achieve desirable robust safety with guaranteed performance in continuous-time nonlinear control applications. They also combine reinforcement learning with control theory to ensure safe learning and optimization. The reinforcement learning-based optimization framework incorporates safety and robustness guarantees by applying theoretical analysis tools from the field of control
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