Abstract--Achieving precise control of robotic tool paths is often challenged by inherent system misalignments, unmodeled dynamics, and actuation inaccuracies. This work introduces an Iterative Learning Control (ILC) strategy to enable precise rotational insertion of a tool during robotic surgery, improving penetration efficacy and safety compared to straight insertion tested in subretinal injection. A 4 degree of freedom (DOF) robot manipulator is used, where misalignment of the fourth joint complicates the simple application of needle rotation, motivating an ILC approach that iteratively adjusts joint commands based on positional feedback. The process begins with calibrating the forward kinematics for the chosen surgical tool to achieve higher accuracy, followed by successive ILC iterations guided by Optical Coherence T omography (OCT) volume scans to measure the error and refine control inputs. Experimental results, tested on subretinal injection tasks on ex vivo pig eyes, show that the optimized trajectory resulted in higher success rates in tissue penetration and subretinal injection compared to straight insertion, demonstrating the effectiveness of ILC in overcoming misalignment challenges. This approach offers potential applications for other high precision robot tasks requiring controlled insertions as well. Accurate and precise control of movement is fundamental to many scientific fields [1], but it becomes even more critical in surgical applications where even minor deviations can significantly impact outcomes. Surgical procedures often demand sub-millimeter accuracy, especially in areas involving delicate tissues and confined spaces, such as ophthalmology. However, consistently achieving this level of precision can be challenging due to the inherent limitations of human motor skills, such as involuntary tremors and fatigue [2]. These limitations are amplified in intraocular microsurgery, requiring not only steady hands, but also enhanced sensory feedback and hand-eye coordination.

Solving motion tasks autonomously and accurately is a core ability for intelligent real-world systems. To achieve genuine autonomy across multiple systems and tasks, key challenges include coping with unknown dynamics and overcoming the need for manual parameter tuning, which is especially crucial in complex Multiple-Input Multiple-Output (MIMO) systems. This paper presents MIMO Dual Iterative Learning Control (DILC), a novel data-driven iterative learning scheme for simultaneous tracking control and model learning, without requiring any prior system knowledge or manual parameter tuning. The method is designed for repetitive MIMO systems and integrates seamlessly with established iterative learning control methods. We provide monotonic convergence conditions for both reference tracking error and model error in linear time-invariant systems. The DILC scheme -- rapidly and autonomously -- solves various motion tasks in high-fidelity simulations of an industrial robot and in multiple nonlinear real-world MIMO systems, without requiring model knowledge or manually tuning the algorithm. In our experiments, many reference tracking tasks are solved within 10-20 trials, and even complex motions are learned in less than 100 iterations. We believe that, because of its rapid and autonomous learning capabilities, DILC has the potential to serve as an efficient building block within complex learning frameworks for intelligent real-world systems.

Achieving precise target jumping with legged robots poses a significant challenge due to the long flight phase and the uncertainties inherent in contact dynamics and hardware. Forcefully attempting these agile motions on hardware could result in severe failures and potential damage. Motivated by these challenging problems, we propose an Iterative Learning Control (ILC) approach that aims to learn and refine jumping skills from easy to difficult, instead of directly learning these challenging tasks. We verify that learning from simplicity can enhance safety and target jumping accuracy over trials. Compared to other ILC approaches for legged locomotion, our method can tackle the problem of a long flight phase where control input is not available. In addition, our approach allows the robot to apply what it learns from a simple jumping task to accomplish more challenging tasks within a few trials directly in hardware, instead of learning from scratch. We validate the method via extensive experiments in the A1 model and hardware for various jumping tasks. Starting from a small jump (e.g., a forward leap of 40cm), our learning approach empowers the robot to accomplish a variety of challenging targets, including jumping onto a 20cm high box, jumping to a greater distance of up to 60cm, as well as performing jumps while carrying an unknown payload of 2kg. Our framework can allow the robot to reach the desired position and orientation targets with approximate errors of 1cm and 1 degree within a few trials.

This paper introduces a unique force control and adaptation algorithm for a lightweight and low-complexity five-fingered robotic hand, namely an Integrated-Finger Robotic Hand (IFRH). The force control and adaptation algorithm is intuitive to design, easy to implement, and improves the grasping functionality through feedforward adaptation automatically. Specifically, we have extended Youla-parameterization which is traditionally used in feedback controller design into a feedforward iterative learning control algorithm (ILC). The uniqueness of such an extension is that both the feedback and feedforward controllers are parameterized over one unified design parameter which can be easily customized based on the desired closed-loop performance. While Youla-parameterization and ILC have been explored in the past on various applications, our unique parameterization and computational methods make the design intuitive and easy to implement. This provides both robust and adaptive learning capabilities, and our application rivals the complexity of many robotic hand control systems. Extensive experimental tests have been conducted to validate the effectiveness of our method.

The sudden onset of deleterious and oscillatory dynamics (often called instabilities) is a known challenge in many fluid, plasma, and aerospace systems. These dynamics are difficult to address because they are nonlinear, chaotic, and are often too fast for active control schemes. In this work, we develop an alternative active controls system using an iterative, trajectory-optimization and parameter-tuning approach based on Iterative Learning Control (ILC), Time-Lagged Phase Portraits (TLPP) and Gaussian Process Regression (GPR). The novelty of this approach is that it can control a system's dynamics despite the controller being much slower than the dynamics. We demonstrate this controller on the Lorenz system of equations where it iteratively adjusts (tunes) the system's input parameters to successfully reproduce a desired oscillatory trajectory or state. Additionally, we investigate the system's dynamical sensitivity to its control parameters, identify continuous and bounded regions of desired dynamical trajectories, and demonstrate that the controller is robust to missing information and uncontrollable parameters as long as certain requirements are met. The controller presented in this work provides a framework for low-speed control for a variety of fast, nonlinear systems that may aid in instability suppression and mitigation.

This work proposes Autonomous Iterative Motion Learning (AI-MOLE), a method that enables systems with unknown, nonlinear dynamics to autonomously learn to solve reference tracking tasks. The method iteratively applies an input trajectory to the unknown dynamics, trains a Gaussian process model based on the experimental data, and utilizes the model to update the input trajectory until desired tracking performance is achieved. Unlike existing approaches, the proposed method determines necessary parameters automatically, i.e., AI-MOLE works plug-and-play and without manual parameter tuning. Furthermore, AI-MOLE only requires input/output information, but can also exploit available state information to accelerate learning. While other approaches are typically only validated in simulation or on a single real-world testbed using manually tuned parameters, we present the unprecedented result of validating the proposed method on three different real-world robots and a total of nine different reference tracking tasks without requiring any a priori model information or manual parameter tuning. Over all systems and tasks, AI-MOLE rapidly learns to track the references without requiring any manual parameter tuning at all, even if only input/output information is available.

Force interaction is inevitable when robots face multiple operation scenarios. How to make the robot competent in force control for generalized operations such as multi-tasks still remains a challenging problem. Aiming at the reproducibility of interaction tasks and the lack of a generalized force control framework for multi-task scenarios, this paper proposes a novel hybrid control framework based on active admittance control with iterative learning parameters-tunning mechanism. The method adopts admittance control as the underlying algorithm to ensure flexibility, and iterative learning as the high-level algorithm to regulate the parameters of the admittance model. The whole algorithm has flexibility and learning ability, which is capable of achieving the goal of excellent versatility. Four representative interactive robot manipulation tasks are chosen to investigate the consistency and generalisability of the proposed method. Experiments are designed to verify the effectiveness of the whole framework, and an average of 98.21% and 91.52% improvement of RMSE is obtained relative to the traditional admittance control as well as the model-free adaptive control, respectively.

It is often necessary for drones to complete delivery, photography, and rescue in the shortest time to increase efficiency. Many autonomous drone races provide platforms to pursue algorithms to finish races as quickly as possible for the above purpose. Unfortunately, existing methods often fail to keep training and racing time short in drone racing competitions. This motivates us to develop a high-efficient learning method by imitating the training experience of top racing drivers. Unlike traditional iterative learning control methods for accurate tracking, the proposed approach iteratively learns a trajectory online to finish the race as quickly as possible. Simulations and experiments using different models show that the proposed approach is model-free and is able to achieve the optimal result with low computation requirements. Furthermore, this approach surpasses some state-of-the-art methods in racing time on a benchmark drone racing platform. An experiment on a real quadcopter is also performed to demonstrate its effectiveness.

The repetitive tracking task for time-varying systems (TVSs) with non-repetitive time-varying parameters, which is also called non-repetitive TVSs, is realized in this paper using iterative learning control (ILC). A machine learning (ML) based nominal model update mechanism, which utilizes the linear regression technique to update the nominal model at each ILC trial only using the current trial information, is proposed for non-repetitive TVSs in order to enhance the ILC performance. Given that the ML mechanism forces the model uncertainties to remain within the ILC robust tolerance, an ILC update law is proposed to deal with non-repetitive TVSs. How to tune parameters inside ML and ILC algorithms to achieve the desired aggregate performance is also provided. The robustness and reliability of the proposed method are verified by simulations. Comparison with current state-of-the-art demonstrates its superior control performance in terms of controlling precision. This paper broadens ILC applications from time-invariant systems to non-repetitive TVSs, adopts ML regression technique to estimate non-repetitive time-varying parameters between two ILC trials and proposes a detailed parameter tuning mechanism to achieve desired performance, which are the main contributions.

A proportional iterative learning control (P-ILC) for linear models of an existing hybrid stroke rehabilitation scheme is implemented for elbow extension/flexion during a rehabilitative task. Owing to transient error growth problem of P-ILC, a learning derivative constraint controller was included to ensure that the controlled system does not exceed a predefined velocity limit at every trial. To achieve this, linear transfer function models of the robot end-effector interaction with a stroke subject (plant) and muscle response to stimulation controllers were developed. A straight-line point-point trajectory of 0 - 0.3 m range served as the reference task space trajectory for the plant, feedforward, and feedback stimulation controllers. At each trial, a SAT-based bounded error derivative ILC algorithm served as the learning constraint controller. Three control configurations were developed and simulated. The system performance was evaluated using the root means square error (RMSE) and normalized RMSE. At different ILC gains over 16 iterations, a displacement error of 0.0060 m was obtained when control configurations were combined.