Quasi-Periodic Gaussian Process Predictive Iterative Learning Control

• URL: http://arxiv.org/abs/2602.18014v1
• Date: Fri, 20 Feb 2026 06:10:10 GMT
• Title: Quasi-Periodic Gaussian Process Predictive Iterative Learning Control
• Authors: Unnati Nigam, Radhendushka Srivastava, Faezeh Marzbanrad, Michael Burke,
• Abstract summary: Iterative learning control (ILC) improves performance by using information from previous iterations to compensate for expected errors in future iterations.<n>This work incorporates the use of Quasi-Periodic Gaussian Processes (QPGPs) into a predictive ILC framework.<n>We benchmark the method against both standard ILC and conventional GP-based predictive ILC on three tasks.
• Score: 7.0206339525686055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Repetitive motion tasks are common in robotics, but performance can degrade over time due to environmental changes and robot wear and tear. Iterative learning control (ILC) improves performance by using information from previous iterations to compensate for expected errors in future iterations. This work incorporates the use of Quasi-Periodic Gaussian Processes (QPGPs) into a predictive ILC framework to model and forecast disturbances and drift across iterations. Using a recent structural equation formulation of QPGPs, the proposed approach enables efficient inference with complexity $\mathcal{O}(p^3)$ instead of $\mathcal{O}(i^2p^3)$, where $p$ denotes the number of points within an iteration and $i$ represents the total number of iterations, specially for larger $i$. This formulation also enables parameter estimation without loss of information, making continual GP learning computationally feasible within the control loop. By predicting next-iteration error profiles rather than relying only on past errors, the controller achieves faster convergence and maintains this under time-varying disturbances. We benchmark the method against both standard ILC and conventional Gaussian Process (GP)-based predictive ILC on three tasks, autonomous vehicle trajectory tracking, a three-link robotic manipulator, and a real-world Stretch robot experiment. Across all cases, the proposed approach converges faster and remains robust under injected and natural disturbances while reducing computational cost. This highlights its practicality across a range of repetitive dynamical systems.

Related papers

• Tail-Aware Post-Training Quantization for 3D Geometry Models [58.79500829118265]
  Post-Training Quantization (PTQ) enables efficient inference without retraining.<n>PTQ fails to transfer effectively to 3D models due to intricate feature distributions and prohibitive calibration overhead.<n>We propose TAPTQ, a Tail-Aware Post-Training Quantization pipeline for 3D geometric learning.
  arXiv  Detail & Related papers  (2026-02-02T07:21:15Z)
• A Class of Accelerated Fixed-Point-Based Methods with Delayed Inexact Oracles and Its Applications [3.6997773420183866]
  We develop a fixed-point-based framework using delayed inexact oracles to approximate a fixed point of a nonexpansive operator.<n>Our approach leverages both Nesterov's acceleration technique and the Krasnosel'skii-Mann (KM) iteration.
  arXiv  Detail & Related papers  (2025-12-15T17:06:22Z)
• Sparse Variable Projection in Robotic Perception: Exploiting Separable Structure for Efficient Nonlinear Optimization [17.673808672099454]
  We present a VarPro scheme for problems with gauge symmetries that jointly exploits separability and sparsity.<n>Our method can be applied as a one-time preprocessing step to construct a emphmatrix-free Schur complement operator.<n>We release an open-source C++ implementation and all datasets from our experiments.
  arXiv  Detail & Related papers  (2025-12-08T19:02:56Z)
• Evolution Strategies at the Hyperscale [57.75314521465674]
  We introduce EGGROLL, an evolution strategies (ES) algorithm designed to scale backprop-free optimization to large population sizes.<n>ES is a set of powerful blackbox optimisation methods that can handle non-differentiable or noisy objectives.<n>EGGROLL overcomes these bottlenecks by generating random matrices $Ain mathbbRmtimes r, Bin mathbbRntimes r$ with $rll min(m,n)$ to form a low-rank matrix perturbation $A Btop$
  arXiv  Detail & Related papers  (2025-11-20T18:56:05Z)
• INC: An Indirect Neural Corrector for Auto-Regressive Hybrid PDE Solvers [61.84396402100827]
  We propose the Indirect Neural Corrector ($mathrmINC$), which integrates learned corrections into the governing equations.<n>$mathrmINC$ reduces the error amplification on the order of $t-1 + L$, where $t$ is the timestep and $L$ the Lipschitz constant.<n>We test $mathrmINC$ in extensive benchmarks, covering numerous differentiable solvers, neural backbones, and test cases ranging from a 1D chaotic system to 3D turbulence.
  arXiv  Detail & Related papers  (2025-11-16T20:14:28Z)
• Efficient Network Automatic Relevance Determination [30.611086842690426]
  Network Automatic Relevance Determination (NARD) is an extension of ARD for linearly probabilistic models.<n>NARD simultaneously model sparse relationships between inputs $X in mathbb Rd times N$ and outputs $Y in mathbb Rm times N$.
  arXiv  Detail & Related papers  (2025-06-14T05:20:25Z)
• Convergence of ease-controlled Random Reshuffling gradient Algorithms under Lipschitz smoothness [0.0]
  We consider the average of a very large number of smooth possibly non-size functions, and we use two widely minibatch frameworks to tackle this problem. We define ease-controlled modifications of IG/RR schemes, which require a light additional computational effort. We prove our implementation with both a full batch gradient (i.e. L-BFGS) and an implementation of IG/RR methods, proving that algorithms require a similar computational effort.
  arXiv  Detail & Related papers  (2022-12-04T15:26:36Z)
• Asynchronous Training Schemes in Distributed Learning with Time Delay [17.259708772713164]
  In the context of distributed deep learning, the issue of stale weights or gradients could result in poor algorithmic performance. In this paper, we propose a different approach to tackle the issue of stale weights or gradients. One practical variant of PC-ASGD is also proposed by adopting a condition to help with the determination of the tradeoff parameter.
  arXiv  Detail & Related papers  (2022-08-28T07:14:59Z)
• Deep Equilibrium Optical Flow Estimation [80.80992684796566]
  Recent state-of-the-art (SOTA) optical flow models use finite-step recurrent update operations to emulate traditional algorithms. These RNNs impose large computation and memory overheads, and are not directly trained to model such stable estimation. We propose deep equilibrium (DEQ) flow estimators, an approach that directly solves for the flow as the infinite-level fixed point of an implicit layer.
  arXiv  Detail & Related papers  (2022-04-18T17:53:44Z)
• Finite-time System Identification and Adaptive Control in Autoregressive Exogenous Systems [79.67879934935661]
  We study the problem of system identification and adaptive control of unknown ARX systems. We provide finite-time learning guarantees for the ARX systems under both open-loop and closed-loop data collection.
  arXiv  Detail & Related papers  (2021-08-26T18:00:00Z)
• Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning [54.01682318834995]
  Preconditioning is a highly effective step for any iterative method involving matrix-vector multiplication. We prove that preconditioning has an additional benefit that has been previously unexplored. It simultaneously can reduce variance at essentially negligible cost.
  arXiv  Detail & Related papers  (2021-07-01T06:43:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.