Differentiable Iterative Learning Control for Black-Box Position-Commanded Manipulators
Differentiable Iterative Learning Control for Black-Box Position-Commanded Manipulators
This research introduces Differentiable Iterative Learning Control (Diff-ILC), an online bilevel framework that adapts constrained iterative learning control parameters between physical robot executions without requiring access to low-level controllers. By identifying a closed-loop differential model from paired smooth perturbations and using hypergradients derived from implicitly differentiating the locally active KKT system, Diff-ILC enables multiple computational parameter updates without additional robot executions. Experiments on a 6-DoF DOBOT CR3 show that after only two trial-to-trial updates, Diff-ILC achieves 78.8% and 76.4% lower mean joint-vector and Cartesian RMSE, respectively, compared to fixed-parameter model-based ILC across four primary trajectories.
The study presents a differentiable iterative learning control framework for black-box manipulators, demonstrating improved convergence and reduced error metrics on a 6-DoF DOBOT CR3 across multiple trajectories.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Data origin
- Robot trajectoriesOpen source S4
Reported numbers
trajectories
4
Reported trials
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enabling multiple computational parameter updates without additional robot executions. Experiments on a 6-DoF DOBOT CR3 across unseen planar and three-dimensional trajectories show substantially faster physical-trial convergence than fixed-parameter model-based ILC. Across the four primary trajectories, after only two trial-to-trial updates, Diff-ILC achieves 78.8% and 76.4% lower mean joint-vector and Cartesian RMSE
Open source S4trial-to-trial updates
2 trials
View original evidence
enabling multiple computational parameter updates without additional robot executions. Experiments on a 6-DoF DOBOT CR3 across unseen planar and three-dimensional trajectories show substantially faster physical-trial convergence than fixed-parameter model-based ILC. Across the four primary trajectories, after only two trial-to-trial updates, Diff-ILC achieves 78.8% and 76.4% lower mean joint-vector and Cartesian RMSE
Open source S4DoF
6
View original evidence
enabling multiple computational parameter updates without additional robot executions. Experiments on a 6-DoF DOBOT CR3 across unseen planar and three-dimensional trajectories show substantially faster physical-trial convergence than fixed-parameter model-based ILC. Across the four primary trajectories, after only two trial-to-trial updates, Diff-ILC achieves 78.8% and 76.4% lower mean joint-vector and Cartesian RMSE
Open source S4
- paper: not_reported
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enabling multiple computational parameter updates without additional robot executions. Experiments on a 6-DoF DOBOT CR3 across unseen planar and three-dimensional trajectories show substantially faster physical-trial convergence than fixed-parameter model-based ILC. Across the four primary trajectories, after only two trial-to-trial updates, Diff-ILC achieves 78.8% and 76.4% lower mean joint-vector and Cartesian RMSE
Open source S4
arXiv:2611.01220v1 Announce Type: new Abstract: High-accuracy repetitive tracking is challenging for position-commanded manipulators whose low-level controllers are inaccessible, while constrained model-based iterative learning control (ILC) can require substantial manual parameter tuning. We present Differentiable Iterative Learning Control (Diff-ILC), an online bilevel framework that adapts the parameters of a cons
Open source S2
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Source：arXiv Robotics — research abstracts · arxiv.org