Generalizing deep reinforcement learning across cable-driven parallel robot configurations with actuator-level policies
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 policies do not generalize well to different robot configurations or varying numbers of actuators. In this article, we introduce a novel DRL approach for controlling CDPRs that does not depend on the specific robot configuration. Our method trains an actuator-level policy that controls each motor to achieve its target cable length, in contrast to conventional DRL approaches that learn to control the entire robot to re...
| Authors | Abir Bouaouda, Mohamed Boutayeb, François Charpillet et al. |
|---|---|
| Primary category | cs.RO |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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