Robotics
Generalizing deep reinforcement learning across cable-driven parallel robot configurations with actuator-level policies
Decoupling robot control from geometry for universal cable-driven parallel robot operation
Cable-driven parallel robots (CDPRs) present diverse configurations and complex control challenges, which can be addressed by deep reinforcement learning (DRL) by learning their nonlinear dynamics. However, DRL methods often require extensive training time, and the resulting poli...
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ArXiv CS.RO
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ArXiv CS.RO
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By shifting reinforcement learning focus from global robot control to individual actuator management, this approach effectively decouples control policies from specific hardware geometry. This breakthrough enables universal deployment across diverse cable-driven robotic systems without needing task-specific retraining.
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