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Data-Driven control of Soft Robots Using Koopman Operator Theory

IEEE TRANSACTIONS ON ROBOTICS 2021年第3期37卷 948-961页

作者： Bruder, Daniel Fu, Xun Gillespie, R. Brent Remy, C. David Vasudevan, Ram Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA Univ Stuttgart Inst Nonlinear Mech D-70174 Stuttgart Germany

controlling soft robots with precision is a challenge due to the difficulty of constructing models that are amenable to model-based control design techniques. Koopman operator theory offers a way to construct explicit dynamical models of soft robots and to control them using established model-based control methods. This approach is data driven, yet yields an explicit control-oriented model rather than just a "black-box" input-output mapping. This work describes a Koopman-based system identification method and its application to model predictive control (MPC) design for soft robots. Three MPC controllers are developed for a pneumatic soft robot arm via the Koopman-based approach, and their performances are evaluated with respect to several real-world trajectory following tasks. In terms of average tracking error, these Koopman-based controllers are more than three times more accurate than a benchmark MPC controller based on a linear state-space model of the same system, demonstrating the utility of the Koopman approach in controlling real soft robots.

关键词： Robots Soft robotics Predictive models Predictive control Aerospace electronics Task analysis Delays Koopman operator model learning for control optimal control soft robots

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Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement learning

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第4期6卷 7262-7269页

作者： Cai, Peide Wang, Hengli Huang, Huaiyang Liu, Yuxuan Liu, Ming Hong Kong Univ Sci & Technol Hong Kong Peoples R China

Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computationally inefficient and sensitive to environmental changes. Recently, deep-learning-based end-to-end systems have shown promising results for autonomous driving/racing. However, they are commonly implemented by supervised imitation learning (IL), which suffers from the distribution mismatch problem, or by reinforcement learning (RL), which requires a huge amount of risky interaction data. In this work, we present a general deep imitative reinforcement learning approach (DIRL), which successfully achieves agile autonomous racing using visual inputs. The driving knowledge is acquired from both IL and model-based RL, where the agent can learn from human teachers as well as perform self-improvement by safely interacting with an offline world model. We validate our algorithm both in a high-fidelity driving simulation and on a real-world 1/20-scale RC-car with limited onboard computation. The evaluation results demonstrate that our method outperforms previous IL and RL methods in terms of sample efficiency and task performance. Demonstration videos are available at https://***/autorace-dirl/.

关键词： Reinforcement learning imitation learning model learning for control autonomous racing uncertainty awareness

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Recurrent Convex Difference Neural Networks for Safety-Critical model Predictive control

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第6期10卷 6400-6407页

作者： Chen, Hanlong Wang, Yang Lin, Wang Ding, Zuohua Zhejiang Sci Tech Univ Sch Comp Sci & Technol Hangzhou 310018 Zhejiang Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China

Optimal control and planning with safety considerations constitute a fundamental challenge in model predictive control (MPC) applications, which has recently been addressed by integrating control Barrier Functions (CBFs) to yield a safety-critical form of MPC, known as MPC-CBF. However, current neural network based approaches often face slow convergence speeds and limited prediction capabilities for MPC-CBFs. To address these limitations, we propose a Recurrent Convex Difference Neural Network (RCDiNN) framework, which can efficiently balance the prediction accuracy and convergence speed for model predictive control. It first incorporates RCDiNN to predict system dynamics and optimize control actions, and then employs a Lagrangian dual deep learning method for RCDiNN training, to encourage the satisfaction of the constraints given by MPC-CBF. We conduct an experimental evaluation on several benchmarks for obstacle avoidance, which demonstrates that our approach is more effective than the existing neural network-based MPC approaches.

关键词： Predictive control System dynamics Safety Optimization Training Accuracy Recurrent neural networks Convergence Computational modeling Optimal control Robot safety model learning for control optimization and optimal control

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Advantages of Bilinear Koopman Realizations for the modeling and control of Systems With Unknown Dynamics

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第3期6卷 4369-4376页

作者： Bruder, Daniel Fu, Xun Vasudevan, Ram Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA Univ Michigan Mech Engn Dept Ann Arbor MI 48109 USA

Nonlinear dynamical systems can be made easier to control by lifting them into the space of observable functions, where their evolution is described by the linear Koopman operator. This letter describes how the Koopman operator can be used to generate approximate linear, bilinear, and nonlinear model realizations from data, and argues in favor of bilinear realizations for characterizing systems with unknown dynamics. Necessary and sufficient conditions for a dynamical system to have a valid linear or bilinear realization over a given set of observable functions are presented and used to show that every control-affine system admits an infinite-dimensional bilinear realization, but does not necessarily admit a linear one. Therefore, approximate bilinear realizations constructed from generic sets of basis functions tend to improve as the number of basis functions increases, whereas approximate linear realizations may not. To demonstrate the advantages of bilinear Koopman realizations for control, a linear, bilinear, and nonlinear Koopman model realization of a simulated robot arm is constructed from data. In a trajectory following task, the bilinear realization exceeds the prediction accuracy of the linear realization and the computational efficiency of the nonlinear realization when incorporated into a model predictive control framework.

关键词： model learning for control

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learning Optimal Impedance control During Complex 3D Arm Movements

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 1248-1255页

作者： Naceri, Abdeldjallil Schumacher, Tobias Li, Qiang Calinon, Sylvain Ritter, Helge Tech Univ Munich TUM Munich Sch Robot & Machine Intelligence MSRM Munich Germany Bielefeld Univ Neuroinformat Grp D-33619 Bielefeld Germany Bielefeld Univ CITEC D-33619 Bielefeld Germany Idiap Res Inst CH-1920 Martigny Switzerland

Humans use their limbs to perform various movements to interact with an external environment. Thanks to limb's variable and adaptive stiffness, humans can adapt their movements to the external unstable dynamics. The underlying adaptive mechanism has been investigated, employing a simple planar device perturbed by external 2D force patterns. In this work, we will employ a more advanced, compliant robot arm to extend previous work to a more realistic 3D-setting. We study the adaptive mechanism and use machine learning to capture the human adaptation behavior. In order to model human's stiffness adaptive skill, we give human subjects the task to reach for a target by moving a handle assembled on the end-effector of a compliant robotic arm. The arm is force controlled and the human is required to navigate the handle inside a non-visible, virtual maze and explore it only through robot force feedback when contacting maze virtual walls. By sampling the hand's position and force data, a computational model based on a combination of model predictive control and nonlinear regression is used to predict participants' successful trials. Our study shows that participants selectively increased the stiffness within the axis direction of uncertainty to compensate for instability caused by a divergent external force field. The learned controller was able to successfully mimic this behavior. When it is deployed on the robot for the navigation task, the robot arm successfully adapt to the unstable dynamics in the virtual maze, in a similar manner as observed in the participants' adaptation skill.

关键词： Force Robots Task analysis Three-dimensional displays Perturbation methods End effectors Dynamics Human stiffness learning human-centered robotics modeling and simulating humans model learning for control machine learning for robot control

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Neural Identification for control

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第3期6卷 4648-4655页

作者： Saha, Priyabrata Egerstedt, Magnus Mukhopadhyay, Saibal Georgia Tech Sch Elect & Comp Engn Atlanta GA 30332 USA

We present a new method for learning control law that stabilizes an unknown nonlinear dynamical system at an equilibrium point. We formulate a system identification task in a self-supervised learning setting that jointly learns a controller and corresponding stable closed-loop dynamics hypothesis. The input-output behavior of the unknown dynamical system under random control inputs is used as the supervising signal to train the neural network-based system model and the controller. The proposed method relies on the Lyapunov stability theory to generate a stable closed-loop dynamics hypothesis and corresponding control law. We demonstrate our method on various nonlinear control problems such as n-link pendulum balancing and trajectory tracking, pendulum on cart balancing, and wheeled vehicle path following.

关键词： Nonlinear dynamical systems Vehicle dynamics System dynamics Lyapunov methods control systems Training data Artificial neural networks Deep learning methods machine learning for robot control model learning for control

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Bayesian Neural Network modeling and Hierarchical MPC for a Tendon-Driven Surgical Robot With Uncertainty Minimization

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 2642-2649页

作者： Cursi, Francesco Modugno, Valerio Lanari, Leonardo Oriolo, Giuseppe Kormushev, Petar Imperial Coll London Hamlyn Ctr Exhibit Rd London England Imperial Coll London Robot Intelligence Lab London England Sapienza Univ Roma Dipartimento Ingn Informat Automat & Gest I-00185 Rome Italy

In order to guarantee precision and safety in robotic surgery, accurate models of the robot and proper control strategies are needed. Bayesian Neural Networks (BNN) are capable of learning complex models and provide information about the uncertainties of the learned system. model Predictive control (MPC) is a reliable control strategy to ensure optimality and satisfaction of safety constraints. In this work we propose the use of BNN to build the highly nonlinear kinematic and dynamic models of a tendon-driven surgical robot, and exploit the information about the epistemic uncertainties by means of a Hierarchical MPC (Hi-MPC) control strategy. Simulation and real world experiments show that the method is capable of ensuring accurate tip positioning, while satisfying imposed safety bounds on the kinematics and dynamics of the robot.

关键词： Medical robots and systems model learning for control robot safety tendon/wire mechanism

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Meta learning With Paired Forward and Inverse models for Efficient Receding Horizon control

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 3240-3247页

作者： McKinnon, Christopher Schoellig, Angela P. Univ Toronto Dynam Syst Lab Inst Aerosp Studies Toronto ON M3H 5T6 Canada Vector Inst Artificial Intelligence Toronto ON M3H 5T6 Canada

This paper presents a model-learning method for Stochastic model Predictive control (SMPC) that is both accurate and computationally efficient. We assume that the control input affects the robot dynamics through an unknown (but invertable) nonlinear function. By learning this unknown function and its inverse, we can use the value of the function as a new control input (which we call the input feature) that is optimised by SMPC in place of the original control input. This removes the need to evaluate a function approximator for the unknown function during optimisation in SMPC (where it would be evaluated many times), reducing the computational cost. The learned inverse is evaluated only once at each sampling time to convert the optimal input feature from SMPC to a control input to apply to the system. We assume that the remaining unknown dynamics can be accurately represented as a model that is linear in a set of coefficients, which enables fast adaptation to new conditions. We demonstrate our approach in experiments on a large ground robot using a stereo camera for localisation.

关键词： Field robots model learning for control robust adaptive control

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Disruption-Resistant Deformable Object Manipulation on Basis of Online Shape Estimation and Prediction-Driven Trajectory Correction

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 3809-3816页

作者： Tanaka, Daisuke Arnold, Solvi Yamazaki, Kimitoshi Shinshu Univ Grad Sch Med Sci & Technol Dept Sci & Technol Nagano 3808553 Japan Shinshu Univ Dept Mech Syst Engn Nagano 3808553 Japan

We consider the problem of deformable object manipulation with variable goal states and mid-manipulation disruptions. We propose an approach that integrates online shape estimation, prediction of shape transitions, and mid-manipulation trajectory correction. All functionalities are implemented using two neural network architectures. We apply this approach to the problem of cloth folding, and perform evaluation experiments in simulation and on robot hardware. We demonstrate that the system can achieve good approximation of given goal states, even when the manipulation process is disrupted by cloth slipping or external interference.

关键词： Deep learning in grasping and manipulation model learning for control neural and fuzzy control dual arm manipulation cloth manipulation

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Using Deep-learning Proximal Policy Optimization to Solve the Inverse Kinematics of Endoscopic Instruments

IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS

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IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 2021年第1期3卷 273-276页

作者： Schmitz, Andreas Berthet-Rayne, Pierre Imperial Coll London Hamlyn Ctr Robot Surg London SW7 2AZ England

There is currently a trend towards small tendon-driven robotic devices targeting endoscopic applications. As these robotic systems aim to navigate within narrow tortuous pathways, their joint arrangement must be longitudinal. Additionally, mechanical constraints such as tendon routing limit the joint range which impacts the overall dexterity of the instruments and complicates the use of iterative inverse kinematics solvers. This article investigates the use of reinforcement learning and proximal policy optimization to solve the inverse kinematics problem of endoscopic instruments while considering joint limits, joint velocity, and whole body configuration. The results show that the proposed approach is able to learn the kinematic model of a 5 degrees of freedom endoscopic instrument and performs better than the Damped Least Square iterative approach in terms of computation time, position error, and orientation error.

关键词： Surgical robotics laparoscopy medical robots and systems model learning for control

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