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Subject-Independent Estimation of Continuous Movements Using CNN-LSTM for a Home-Based Upper Limb Rehabilitation System

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第10期8卷 6403-6410页

作者： Li, He Guo, Shuxiang Bu, Dongdong Wang, Hanze Kawanishi, Masahiko Beijing Inst Technol Sch Life Sci Beijing 100081 Peoples R China Beijing Inst Technol Minist Ind & Informat Technol Key Lab Convergence Biomed Engn Syst & Healthcare Beijing 100081 Peoples R China Kagawa Univ Intelligent Mech Syst Engn Dept Takamatsu 7610793 Japan Kagawa Univ Fac Med Dept Neurol Surg Takamatsu 7610793 Japan

Exoskeleton-assisted home-based rehabilitation plays a vital role in the upper limb rehabilitation of stroke patients in early stage. The surface electromyography (sEMG)-based control can facilitate friendly interactions between individuals and rehabilitation exoskeletons. The exoskeleton can also meet the requirements of home-based rehabilitation, including affordability, portability, safety, and active participation. Although various systems have been proposed to enhance upper limb training, few studies have addressed the inter-subject variability of sEMG signals, which limits the generalization capability of the intention estimation model. In this letter, a subject-independent continuous motion estimation method combining convolutional neural networks (CNN) and long and short-term memory (LSTM) is proposed and applied to a home-based bilateral training system. The sEMG-driven CNN-LSTM model builds the relationship between sEMG signals and continuous movements. To verify the effectiveness of the CNN-LSTM model in achieving subject-independent estimation, the offline estimation under the backpropagation neural network, CNN, and CNN-LSTM are compared. Moreover, the online intention estimation and the real-time control are performed, and the estimation angle error and time delay are controlled at approximately 10 & DEG;and 300 ms, proving the feasibility of the subject-independent estimation method and its availability in the upper-limb rehabilitation system.

关键词： Training Exoskeletons Estimation Brushless motors Real-time systems Convolutional neural networks Elbow Intention recognition rehabilitation robotics deep learning methods model learning for control motion control

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Neural Joint Space Implicit Signed Distance Functions for Reactive Robot Manipulator control

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第2期8卷 480-487页

作者： Koptev, Mikhail Figueroa, Nadia Billard, Aude Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland Univ Penn Philadelphia PA 19104 USA

In this letter, we present an approach for learning a neural implicit signed distance function expressed in joint space coordinates, that efficiently computes distance-to-collisions for arbitrary robotic manipulator configurations. Computing such distances is a long standing problem in robotics as approximate representations of the robot and environment geometry can lead to overly conservative constraints, numerical instabilities and expensive computations - limiting real-time reactive control and task success. Leveraging GPU parallelization and the differentiable nature of the proposed distance function allows for fast calculation of gradients with respect to the neural network inputs, providing a continuous repulsive vector field directly in joint space. We show that the learned high-resolution collision representation can be used to achieve real-time reactive control by i) formulating it as a collision-avoidance constraint for a quadratic programming (QP) inverse kinematics (IK), and ii) introducing it as a collision cost in a sampling-based joint space model predictive controller (MPC). For a reaching benchmark task with a 7DoF robot and dynamic obstacles intentionally obstructing the robot's path we achieve average 250 Hz control frequency with QP-IK and 92 Hz with MPC, showing an accelerated performance of 15% for QP-IK and 40% for MPC over baseline distance computation techniques.

关键词： Collision Avoidance Reactive and Sensor-Based Planning model learning for control

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Reinforcement learning With Energy-Exchange Dynamics for Spring-Loaded Biped Robot Walking

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第10期8卷 6243-6250页

作者： Kuo, Cheng-Yu Shin, Hirofumi Matsubara, Takamitsu Nara Inst Sci & Technol Grad Sch Sci & Technol Nara 6300192 Japan Honda R&D Ltd Saitama 3510114 Japan

This letter presents a probabilistic model-based Reinforcement learning (MBRL) approach for learning the Energy-exchange Dynamics (EED) of a spring-loaded biped robot. Our approach enables on-site walking acquisition with high sample efficiency, real-time planning capability, and generalizability across skill conditions. Specifically, we learn the data-driven state transition dynamics of the robot in the formulation of energy-states, with their interaction characterized as energy-exchange to reduce dimensionality. To improve planning reliability with the learned EED, we design a control space based on a walking trajectory that follows the law of conservation of energy and is formulated by energy-states. We evaluated our approach using a four-degree-of-freedom spring-loaded biped robot in simulation and hardware, and generalizability is validated by using the same learning framework for different walking speeds and terrains in simulation and walking acquisition with hardware. All results showed successful on-site walking acquisition with a compact nine-dimension dynamics model, 40 Hz real-time planning, and on-site learning within a few minutes.

关键词： Humanoid and bipedal locomotion model learning for control reinforcement learning

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Convex Approach to Data-Driven Off-Road Navigation via Linear Transfer Operators

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第6期8卷 3278-3285页

作者： Moyalan, Joseph Chen, Yongxin Vaidya, Umesh Clemson Univ Dept Mech Engn Clemson SC 29634 USA Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30339 USA

We consider the problem of optimal control design for navigation on off-road terrain. We use a traversability measure to characterize the difficulty of navigation on off-road terrain. The traversability measure captures terrain properties essential for navigation, such as elevation maps, roughness, slope, and texture. The terrain with the presence or absence of obstacles becomes a particular case of the proposed traversability measure. We provide a convex formulation to the off-road navigation problem by lifting the problem to the density space using the linear Perron-Frobenius (P-F) operator. The convex formulation leads to an infinite-dimensional optimal navigation problem for control synthesis. We construct the finite-dimensional approximation of the optimization problem using data. We use a computational framework based on the data-driven approximation of the Koopman operator. This makes the proposed approach data-driven and applicable to cases where an explicit system model is unavailable. Finally, we apply the proposed navigation framework with single integrator dynamics and Dubin's car model.

关键词： Navigation Cost function Costs Trajectory Aerospace electronics Optimal control Vehicle dynamics Motion and Path Planning Optimization and Optimal control model learning for control

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model-Agnostic Personalized Knowledge Adaptation for Soft Exoskeleton Robot

IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS

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IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 2023年第2期5卷 353-362页

作者： Li, Ning Chen, Wenyuan Yang, Yang Wang, Yihan Yang, Tie Yu, Peng Zhang, Chuang Wang, Wenxue Xi, Ning Liu, Lianqing Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110169 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Hong Kong Emerging Technol Inst Dept Ind & Mfg Syst Engn Hong Kong Peoples R China

Personalized control strategies can improve the overall performance of exoskeleton robots by providing adaptive assistance to different individuals. However, current personalized strategies require accurate models or large amounts of training data, which are not suitable for soft exoskeletons due to human-robot interaction uncertainties and low data acquisition efficiency. This paper presents a framework of model-Agnostic Personalized Knowledge Adaptation (MPKA) consisting of two modules of Soft-to-Rigid Kinematics model (SRKM) and Data-Driven Personalized Adaptation (DDPA) to generate a personalized strategy. Specifically, the first SRKM module is used to simplify and convert uncertain and time-varying HRI model to a certain and fixed model. The second DDPA module is used to accelerate model adaptation to new individuals using meta-learning with less data, based on the learned knowledge from the previous subjects. The experiments were conducted on three different sized platforms, four healthy and one post-stroke subjects, respectively. The results demonstrate that the MPKA can generate personalized models for new individuals more rapidly with less data than a pre-trained neural network, and the personalized models can obtain better control accuracy by over 62% than mechanism-based mathematical model. The proposed method has the potential to provide personalized rehabilitation strategies, which can improve effectiveness of rehabilitation.

关键词： Exoskeletons Adaptation models Task analysis Robots Human-robot interaction modeling Biological system modeling model learning for control physical human-robot interaction soft exoskeletons rehabilitation robotics

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Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in model-Predictive control 6

Data-efficient, Explainable and Safe Box Manipulation: Illus...

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6th Annual learning for Dynamics and control Conference

作者： Salehi, Achkan Doncieux, Stephane Sorbonne Univ CNRS ISIR Paris France

model-based RL/control have gained significant traction in robotics. Yet, these approaches often remain data-inefficient and lack the explainability of hand-engineered solutions. This makes them difficult to debug/integrate in safety-critical settings. However, in many systems, prior knowledge of environment kinematics/dynamics is available. Incorporating such priors can help address the aforementioned problems by reducing problem complexity and the need for exploration, while also facilitating the expression of the decisions taken by the agent in terms of physically meaningful entities. Our aim with this paper is to illustrate and support this point of view via a case-study. We model a payload manipulation problem based on a real robotic system, and show that leveraging prior knowledge about the dynamics of the environment in an MPC framework can lead to improvements in explainability, safety and data-efficiency, leading to satisfying generalization properties with less data.

关键词： Machine learning for control model learning for control

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ACD-EDMD: Analytical Construction for Dictionaries of Lifting Functions in Koopman Operator-Based Nonlinear Robotic Systems

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 906-913页

作者： Shi, Lu Karydis, Konstantinos Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

Koopman operator theory has been gaining momentum for model extraction, planning, and control of data-driven robotic systems. The Koopman operator's ability to extract dynamics from data depends heavily on the selection of' an appropriate dictionary of lifting functions. In this letter, we propose ACDEDMD, a new method for Analytical Construction of Dictionaries of appropriate lifting functions for a range of data-driven Koopman operator based nonlinear robotic systems. The key insight of this work is that information about fundamental topological spaces of the nonlinear system (such as its configuration space and workspace) can be exploited to steer the construction of Hermite polynomial-based lifting functions. We show that the proposed method leads to dictionaries that are simple to implement while enjoying provable completeness and convergence guarantees when observables are weighted bounded. We evaluate ACD-EDMD using a range of diverse nonlinear robotic systems in both simulated and physical hardware experimentation (a wheeled mobile robot, a two-revolute-joint robotic arm, and a soft robotic leg). Results reveal that our method leads to dictionaries that enable high-accuracy prediction and that can generalize to diverse validation sets. The associated GitHub repository of our algorithm can be accessed at https://***/UCR-Robotics/ACD-EDMD.

关键词： Calibration and identification model learning for control machine learning for robot control koopman operator extended dynamic mode decomposition

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Augmented Neural Network for Full Robot Kinematic modelling in SE(3)

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第3期7卷 7140-7147页

作者： Cursi, Francesco Bai, Weibang Li, Weiyi Yeatman, Eric M. Kormushev, Petar Imperial Coll London Hamlyn Ctr London SW7 2BX England Imperial Coll London Robot Intelligence Lab London SW7 2BX England

Due to the increasing complexity of robotic structures, modelling robots is becoming more and more challenging, and analytical models are very difficult to build. Machine learning approaches have shown great capabilities in learning complex mapping and have widely been used in robot model learning and control. Generally, the inverse kinematics is directly learned, yet, learning the forward kinematics is simpler and allows computing exploiting the optimality of the controllers. Nevertheless, the learning method has no knowledge about the differential relationship between the position and velocity mappings. Currently, few works have targeted learning full robot poses considering both position and orientation. In this letter, we present a novel feedforward Artificial Neural network (ANN) architecture to learn full robot pose in SE(3) incorporating differential relationships in the learning process. Simulation and real world experiments show the capabilities of the proposed network to properly model the robot pose and its advantages over standard ANN.

关键词： model learning for control machine learning for robot control kinematics

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Deep Koopman Operator With control for Nonlinear Systems

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第3期7卷 7700-7707页

作者： Shi, Haojie Meng, Max Q-H Chinese Univ Hong Kong Cent Ave Hong Kong Peoples R China Shenzhen Key Lab Robot Percept Intelligence Shenzhen 518055 Peoples R China Southern Univ Sci Dept Elect Technol Elect Engn Shenzhen 518055 Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Shenzhen 518057 Peoples R China

Recently Koopman operator has become a promising data-driven tool to facilitate real-time control for unknown nonlinear systems. It maps nonlinear systems into equivalent linear systems in embedding space, ready for real-time linear control methods. However, designing an appropriate Koopman embedding function remains a challenging task. Furthermore, most Koopman-based algorithms only consider nonlinear systems with linear control input, resulting in lousy prediction and control performance when the system is fully nonlinear with the control input. In this work, we propose an end-to-end deep learning framework to learn the Koopman embedding function and Koopman Operator together to alleviate such difficulties. We first parameterize the embedding function and Koopman Operator with the neural network and train them end-to-end with the K-steps loss function. Then, an auxiliary control network is augmented to encode the nonlinear state-dependent control term to model the nonlinearity in the control input. This encoded term is considered the new control variable instead to ensure linearity of the modeled system in the embedding system. We next deploy Linear Quadratic Regulator (LQR) on the linear embedding space to derive the optimal control policy and decode the actual control input from the control net. Experimental results demonstrate that our approach outperforms other existing methods, reducing the prediction error by order of magnitude and achieving superior control performance in several nonlinear dynamic systems like damping pendulum, CartPole, and the seven DOF robotic manipulator.

关键词： Deep learning methods model learning for control machine learning for robot control

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KNODE-MPC: A Knowledge-Based Data-Driven Predictive control Framework for Aerial Robots

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 2819-2826页

作者： Chee, Kong Yao Jiahao, Tom Z. Hsieh, M. Ani Univ Penn Grasp Lab Philadelphia PA 19104 USA

In this letter, we consider the problem of deriving and incorporating accurate dynamic models for model predictive control (MPC) with an application to quadrotor control. MPC relies on precise dynamic models to achieve the desired closed-loop performance. However, the presence of uncertainties in complex systems and the environments they operate in poses a challenge in obtaining sufficiently accurate representations of the system dynamics. In this letter, we make use of a deep learning tool, knowledge-based neural ordinary differential equations (KNODE), to augment a model obtained from first principles. The resulting hybrid model encompasses both a nominal first-principle model and a neural network learnt from simulated or real-world experimental data. Using a quadrotor, we benchmark our hybrid model against a state-of-the-art Gaussian Process (GP) model and show that the hybrid model provides more accurate predictions of the quadrotor dynamics and is able to generalize beyond the training data. To improve closed-loop performance, the hybrid model is integrated into a novel MPC framework, known as KNODE-MPC. Results show that the integrated framework achieves 60.2% improvement in simulations and more than 21% in physical experiments, in terms of trajectory tracking performance.

关键词： Machine learning for robot control model learning for control model predictive control

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