Article

RILC: Reinforced Iterative Learning Control for Elastic-Joint Robotic Arms, Achieving Cross-Trajectory Generalization with Convergence Guarantees

This study proposes Reinforced Iterative Learning Control (RILC), which couples Iterative Learning Control (ILC) with Reinforcement Learning (RL) for trajectory tracking of elastic-joint robots, addressing the problems that traditional ILC must learn each new trajectory separately and that RL has high sample complexity and limited tracking accuracy.

Part of the event: Reinforced Iterative Learning Control Method Proposed for Flexible-Joint Arms

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