Event
Reinforced Iterative Learning Control Method Proposed for Flexible-Joint Arms
1 reports · 1 sources
Yuri De Santis et al. proposed Reinforced Iterative Learning Control (RILC), which couples iterative learning control with reinforcement learning for trajectory tracking of flexible-joint robotic arms, and is claimed to enable fast adaptation across trajectories with convergence guarantees. The method addresses the problems that conventional ILC must learn each new trajectory separately, and that RL has high sample complexity and limited tracking accuracy. The study was validated through simulation and experiments on an underactuated 4-degree-of-freedom series-elastic robot, covering different trajectories and modeling mismatch scenarios. The paper has been accepted by Control Engineering Practice (2026).
Report timeline
- 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.
arXiv cs.RO ·