Relevant bibliographies by topics / Control and learning of soft robots

Academic literature on the topic 'Control and learning of soft robots'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Control and learning of soft robots"

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Iscen, Atil, Ken Caluwaerts, Jonathan Bruce, Adrian Agogino, Vytas SunSpiral, and Kagan Tumer. "Learning Tensegrity Locomotion Using Open-Loop Control Signals and Coevolutionary Algorithms." Artificial Life 21, no. 2 (May 2015): 119–40. http://dx.doi.org/10.1162/artl_a_00163.

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Soft robots offer many advantages over traditional rigid robots. However, soft robots can be difficult to control with standard control methods. Fortunately, evolutionary algorithms can offer an elegant solution to this problem. Instead of creating controls to handle the intricate dynamics of these robots, we can simply evolve the controls using a simulation to provide an evaluation function. In this article, we show how such a control paradigm can be applied to an emerging field within soft robotics: robots based on tensegrity structures. We take the model of the Spherical Underactuated Planetary Exploration Robot ball (SUPERball), an icosahedron tensegrity robot under production at NASA Ames Research Center, develop a rolling locomotion algorithm, and study the learned behavior using an accurate model of the SUPERball simulated in the NASA Tensegrity Robotics Toolkit. We first present the historical-average fitness-shaping algorithm for coevolutionary algorithms to speed up learning while favoring robustness over optimality. Second, we use a distributed control approach by coevolving open-loop control signals for each controller. Being simple and distributed, open-loop controllers can be readily implemented on SUPERball hardware without the need for sensor information or precise coordination. We analyze signals of different complexities and frequencies. Among the learned policies, we take one of the best and use it to analyze different aspects of the rolling gait, such as lengths, tensions, and energy consumption. We also discuss the correlation between the signals controlling different parts of the tensegrity robot.
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Kim, Daekyum, Sang-Hun Kim, Taekyoung Kim, Brian Byunghyun Kang, Minhyuk Lee, Wookeun Park, Subyeong Ku, et al. "Review of machine learning methods in soft robotics." PLOS ONE 16, no. 2 (February 18, 2021): e0246102. http://dx.doi.org/10.1371/journal.pone.0246102.

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Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.
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Caremel, Cedric, Matthew Ishige, Tung D. Ta, and Yoshihiro Kawahara. "Echo State Network for Soft Actuator Control." Journal of Robotics and Mechatronics 34, no. 2 (April 20, 2022): 413–21. http://dx.doi.org/10.20965/jrm.2022.p0413.

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Conventional model theories are not suitable to control soft-bodied robots as deformable materials present rapidly changing behaviors. Neuromorphic electronics are now entering the field of robotics, demonstrating that a highly integrated device can mimic the fundamental properties of a sensory synaptic system, including learning and proprioception. This research work focuses on the physical implementation of a reservoir computing-based network to actuate a soft-bodied robot. More specifically, modeling the hysteresis of a shape memory alloy (SMA) using echo state networks (ESN) in real-world situations represents a novel approach to enable soft machines with task-learning. In this work, we show that not only does our ESN model enable our SMA-based robot with locomotion, but it also discovers a successful strategy to do so. Compared to standard control modeling, established either by theoretical frameworks or from experimental data, here, we gained knowledge a posteriori, guided by the physical interactions between the trained model and the controlled actuator, interactions from which striking patterns emerged, and informed us about what type of locomotion would work best for our robot.
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Hamaya, Masashi, Kazutoshi Tanaka, Felix von Drigalski, and Yoshihisa Ijiri. "Learning Control with Soft Robots: Application for Industrial Assembly." Journal of the Robotics Society of Japan 39, no. 7 (2021): 609–12. http://dx.doi.org/10.7210/jrsj.39.609.

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Sun, Boai, Weikun Li, Zhangyuan Wang, Yunpeng Zhu, Qu He, Xinyan Guan, Guangmin Dai, et al. "Recent Progress in Modeling and Control of Bio-Inspired Fish Robots." Journal of Marine Science and Engineering 10, no. 6 (June 2, 2022): 773. http://dx.doi.org/10.3390/jmse10060773.

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Compared with traditional underwater vehicles, bio-inspired fish robots have the advantages of high efficiency, high maneuverability, low noise, and minor fluid disturbance. Therefore, they have gained an increasing research interest, which has led to a great deal of remarkable progress theoretically and practically in recent years. In this review, we first highlight our enhanced scientific understanding of bio-inspired propulsion and sensing underwater and then present the research progress and performance characteristics of different bio-inspired robot fish, classified by the propulsion method. Like the natural fish species they imitate, different types of bionic fish have different morphological structures and distinctive hydrodynamic properties. In addition, we select two pioneering directions about soft robotic control and multi-phase robotics. The hybrid dynamic control of soft robotic systems combines the accuracy of model-based control and the efficiency of model-free control, and is considered the proper way to optimize the classical control model with the intersection of multiple machine learning algorithms. Multi-phase robots provide a broader scope of application compared to ordinary bionic robot fish, with the ability of operating in air or on land outside the fluid. By introducing recent progress in related fields, we summarize the advantages and challenges of soft robotic control and multi-phase robotics, guiding the further development of bionic aquatic robots.
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Van Meerbeek, I. M., C. M. De Sa, and R. F. Shepherd. "Soft optoelectronic sensory foams with proprioception." Science Robotics 3, no. 24 (November 28, 2018): eaau2489. http://dx.doi.org/10.1126/scirobotics.aau2489.

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In a step toward soft robot proprioception, and therefore better control, this paper presents an internally illuminated elastomer foam that has been trained to detect its own deformation through machine learning techniques. Optical fibers transmitted light into the foam and simultaneously received diffuse waves from internal reflection. The diffuse reflected light was interpreted by machine learning techniques to predict whether the foam was twisted clockwise, twisted counterclockwise, bent up, or bent down. Machine learning techniques were also used to predict the magnitude of the deformation type. On new data points, the model predicted the type of deformation with 100% accuracy and the magnitude of the deformation with a mean absolute error of 0.06°. This capability may impart soft robots with more complete proprioception, enabling them to be reliably controlled and responsive to external stimuli.
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Dai, Yicheng, Zhihao Deng, Xin Wang, and Han Yuan. "A Hybrid Controller for a Soft Pneumatic Manipulator Based on Model Predictive Control and Iterative Learning Control." Sensors 23, no. 3 (January 22, 2023): 1272. http://dx.doi.org/10.3390/s23031272.

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Due to the outstanding characteristics of the large structural flexibility and strong dexterity of soft robots, they have attracted great attention. However, the dynamic modeling and precise control of soft robots face huge challenges. Traditional model-based and model-free control methods find it difficult to obtain a balance between complexity and accuracy. In this paper, a dynamic model of a three-chamber continuous pneumatic manipulator is established based on the modal method. Moreover, a hybrid controller integrating model predictive control (MPC) and iterative learning control (ILC) is proposed, which can simultaneously perform model parameter learning and trajectory tracking control. Experimental results show that the proposed control method can optimize the parameters of the dynamic model in real time with less iterations than the traditional model-free method and have good control performance in trajectory tracking experiments. In the future, the proposed dynamic model and the hybrid controller should be verified on a multi-section manipulator.
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Jiang, Hao, Zhanchi Wang, Yusong Jin, Xiaotong Chen, Peijin Li, Yinghao Gan, Sen Lin, and Xiaoping Chen. "Hierarchical control of soft manipulators towards unstructured interactions." International Journal of Robotics Research 40, no. 1 (January 2021): 411–34. http://dx.doi.org/10.1177/0278364920979367.

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Performing daily interaction tasks such as opening doors and pulling drawers in unstructured environments is a challenging problem for robots. The emergence of soft-bodied robots brings a new perspective to solving this problem. In this paper, inspired by humans performing interaction tasks through simple behaviors, we propose a hierarchical control system for soft arms, in which the low-level controller achieves motion control of the arm tip, the high-level controller controls the behaviors of the arm based on the low-level controller, and the top-level planner chooses what behaviors should be taken according to tasks. To realize the motion control of the soft arm in interacting with environments, we propose two control methods. The first is a feedback control method based on a simplified Jacobian model utilizing the motion laws of the soft arm that are not affected by environments during interaction. The second is a control method based on [Formula: see text]-learning, in which we present a novel method to increase training data by setting virtual goals. We implement the hierarchical control system on a platform with the Honeycomb Pneumatic Networks Arm (HPN Arm) and validate the effectiveness of this system on a series of typical daily interaction tasks, which demonstrates this proposed hierarchical control system could render the soft arms to perform interaction tasks as simply as humans, without force sensors or accurate models of the environments. This work provides a new direction for the application of soft-bodied arms and offers a new perspective for the physical interactions between robots and environments.
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Pletl, Szilveszter, and Bela Lantos. "Advanced Robot Control Algorithms Based on Fuzzy, Neural and Genetic Methods." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 2 (March 20, 2001): 81–89. http://dx.doi.org/10.20965/jaciii.2001.p0081.

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Soft computing (fuzzy systems, neural networks and genetic algorithms) can solve difficult problems, especially non-linear control problems such as robot control. In the paper two algorithms have been presented for the nonlinear control of robots. The first algorithm applies a new neural network based controller structure and a learning method with stability guarantee. The controller consists of the nonlinear prefilter, the feedforward neural network and feadback PD controllers. The fast learning algorithm of the neural network is based on Moore-Penrose pseudoinverse technique. The second algorithm is based on a decentralized hierarchical neuro-fuzzy controller structure. New approach to evolutionary algorithms called LEGA optimizes the controller during the teaching period. LEGA combines the standard GA technique with numerical optimum seeking for a limited number of elite individuels in each generation. It can lead to global optimum in few generations. The soft computing based nonlinear control algorithms have been applied for the control of a rigid link flexible joint (RLFJ) 4 DOF SCARA robot in order to prove the effectiveness of the proposed methods.
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KAWAMURA, KAZUHIKO, R. ALAN PETERS II, ROBERT E. BODENHEIMER, NILANJAN SARKAR, JUYI PARK, CHARLES A. CLIFTON, ALBERT W. SPRATLEY, and KIMBERLY A. HAMBUCHEN. "A PARALLEL DISTRIBUTED COGNITIVE CONTROL SYSTEM FOR A HUMANOID ROBOT." International Journal of Humanoid Robotics 01, no. 01 (March 2004): 65–93. http://dx.doi.org/10.1142/s021984360400006x.

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During the last decade, researchers at Vanderbilt have been developing a humanoid robot called the Intelligent Soft Arm Control (ISAC). This paper describes ISAC in terms of its software components and with respect to the design philosophy that has evolved over the course of its development. Central to the control system is a parallel, distributed software architecture, comprising a set of independent software objects or agents that execute as needed on standard PCs linked via Ethernet. Fundamental to the design philosophy is the direct physical interaction of the robot with people. Initially, this philosophy guided application development. Yet over time it became apparent that such interaction may be necessary for the acquisition of intelligent behaviors by an agent in a human-centered environment. Concurrent to that evolution was a shift from a programmer's high-level specification of action toward the robot's own motion acquisition of primitive behaviors through sensory-motor coordination (SMC) and task learning through cognitive control and working memory. Described is the parallel distributed cognitive control architecture and the advantages and limitations that have guided its development. Primary structures for sensing, memory, and cognition are described. Motion learning through teleoperation and fault diagnosis through system health monitoring are also covered. The generality of the control system is discussed in terms of its applicability to physically heterogeneous robots and multi-robot systems.
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Dissertations / Theses on the topic "Control and learning of soft robots"

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Pajon, Adrien. "Humanoid robots walking with soft soles." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTS060/document.

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Lorsque des changements inattendus de la surface du sol se produisent lors de la marche, le système nerveux central humain doit appliquer des mesures de contrôle appropriées pour assurer une stabilité dynamique. De nombreuses études dans le domaine de la commande moteur ont étudié les mécanismes d'un tel contrôle postural et ont largement décrit comment les trajectoires du centre de masse (COM), le placement des pas et l'activité musculaire s'adaptent pour éviter une perte d'équilibre. Les mesures que nous avons effectuées montrent qu'en arrivant sur un sol mou, les participants ont modulé de façon active les forces de réaction au sol (GRF) sous le pied de support afin d'exploiter les propriétés élastiques et déformables de la surface pour amortir l'impact et probablement dissiper l'énergie mécanique accumulée pendant la ‘chute’ sur la nouvelle surface déformable. Afin de contrôler plus efficacement l'interaction pieds-sol des robots humanoïdes pendant la marche, nous proposons d'ajouter des semelles extérieures souples (c'est-à-dire déformables) aux pieds. Elles absorbent les impacts et limitent les effets des irrégularités du sol pendant le mouvement sur des terrains accidentés. Cependant, ils introduisent des degrés de liberté passifs (déformations sous les pieds) qui complexifient les tâches d'estimation de l'état du robot et ainsi que sa stabilisation globale. Pour résoudre ce problème, nous avons conçu un nouveau générateur de modèle de marche (WPG) basé sur une minimisation de la consommation d'énergie qui génère les paramètres nécessaires pour utiliser conjointement un estimateur de déformation basé sur un modèle éléments finis (FEM) de la semelle souple pour prendre en compte sa déformation lors du mouvement. Un tel modèle FEM est coûteux en temps de calcul et empêche la réactivité en ligne. Par conséquent, nous avons développé une boucle de contrôle qui stabilise les robots humanoïdes lors de la marche avec des semelles souples sur terrain plat et irrégulier. Notre contrôleur en boucle fermée minimise les erreurs sur le centre de masse (COM) et le point de moment nul (ZMP) avec un contrôle en admittance des pieds basé sur un estimateur de déformation simplifié. Nous démontrons son efficacité expérimentalement en faisant marcher le robot humanoïde HRP-4 sur des graviers
When unexpected changes of the ground surface occur while walking, the human central nervous system needs to apply appropriate control actions to assure dynamic stability. Many studies in the motor control field have investigated the mechanisms of such a postural control and have widely described how center of mass (COM) trajectories, step patterns and muscle activity adapt to avoid loss of balance. Measurements we conducted show that when stepping over a soft ground, participants actively modulated the ground reaction forces (GRF) under the supporting foot in order to exploit the elastic and compliant properties of the surface to dampen the impact and to likely dissipate the mechanical energy accumulated during the ‘fall’ onto the new compliant surface.In order to control more efficiently the feet-ground interaction of humanoid robots during walking, we propose adding outer soft (i.e. compliant) soles to the feet. They absorb impacts and cast ground unevenness during locomotion on rough terrains. However, they introduce passive degrees of freedom (deformations under the feet) that complexify the tasks of state estimation and overall robot stabilization. To address this problem, we devised a new walking pattern generator (WPG) based on a minimization of the energy consumption that offers the necessary parameters to be used jointly with a sole deformation estimator based on finite element model (FEM) of the soft sole to take into account the sole deformation during the motion. Such FEM computation is time costly and inhibit online reactivity. Hence, we developed a control loop that stabilizes humanoid robots when walking with soft soles on flat and uneven terrain. Our closed-loop controller minimizes the errors on the center of mass (COM) and the zero-moment point (ZMP) with an admittance control of the feet based on a simple deformation estimator. We demonstrate its effectiveness in real experiments on the HRP-4 humanoid walking on gravels
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Kraus, Dustan Paul. "Coordinated, Multi-Arm Manipulation with Soft Robots." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7066.

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Soft lightweight robots provide an inherently safe solution to using robots in unmodeled environments by maintaining safety without increasing cost through expensive sensors. Unfortunately, many practical problems still need to be addressed before soft robots can become useful in real world tasks. Unlike traditional robots, soft robot geometry is not constant but can change with deflation and reinflation. Small errors in a robot's kinematic model can result in large errors in pose estimation of the end effector. This error, coupled with the inherent compliance of soft robots and the difficulty of soft robot joint angle sensing, makes it very challenging to accurately control the end effector of a soft robot in task space. However, this inherent compliance means that soft robots lend themselves nicely to coordinated multi-arm manipulation tasks, as deviations in end effector pose do not result in large force buildup in the arms or in the object being manipulated. Coordinated, multi-arm manipulation with soft robots is the focus of this thesis. We first developed two tools enabling multi-arm manipulation with soft robots: (1) a hybrid servoing control scheme for task space control of soft robot arms, and (2) a general base placement optimization for the robot arms in a multi-arm manipulation task. Using these tools, we then developed and implemented a simple multi-arm control scheme. The hybrid servoing control scheme combines inverse kinematics, joint angle control, and task space servoing in order to reduce end effector pose error. We implemented this control scheme on two soft robots and demonstrated its effectiveness in task space control. Having developed a task space controller for soft robots, we then approached the problem of multi-arm manipulation. The placement of each arm for a multi-arm task is non-trivial. We developed an evolutionary optimization that finds the optimal arm base location for any number of user-defined arms in a user-defined task or workspace. We demonstrated the utility of this optimization in simulation, and then used it to determine the arm base locations for two arms in two real world coordinated multi-arm manipulation tasks. Finally, we developed a simple multi-arm control scheme for soft robots and demonstrated its effectiveness using one soft robot arm, and one rigid robot with low-impedance torque control. We placed each arm base in the pose determined by the base placement optimization, and then used the hybrid servoing controller in our multi-arm control scheme to manipulate an object through two desired trajectories.
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Kandhari, Akhil. "Control and Analysis of Soft Body Locomotion on a Robotic Platform." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1579793861351961.

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Marchese, Andrew D. (Andrew Dominic). "Design, fabrication, and control of soft robots with fluidic elastomer actuators." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97807.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 223-236).
The goal of this thesis is to explore how autonomous robotic systems can be created with soft elastomer bodies powered by fluids. In this thesis we innovate in the design, fabrication, control, and experimental validation of both single and multi-segment soft fluidic elastomer robots. First, this thesis describes an autonomous fluidic elastomer robot that is both self-contained and capable of rapid, continuum body motion. Specifically, the design, modeling, fabrication, and control of a soft fish is detailed, focusing on enabling the robot to perform rapid escape responses. The robot employs a compliant body with embedded actuators emulating the slender anatomical form of a fish. In addition, the robot has a novel fluidic actuation system that drives body motion and has all the subsystems of a traditional robot on-board: power, actuation, processing, and control. At the core of the fish's soft body is an array of Fluidic Elastomer Actuators (FEAs). The fish is designed to emulate escape responses in addition to forward swimming because such maneuvers require rapid body accelerations and continuum body motion. These maneuvers showcase the performance capabilities of this self-contained robot. The kinematics and controllability of the robot during simulated escape response maneuvers are analyzed and compared to studies on biological fish. During escape responses, the soft-bodied robot is shown to have similar input-output relationships to those observed in biological fish. The major implication of this portion of the thesis is that a soft fluidic elastomer robot is shown to be both self-contained and capable of rapid body motion. Next, this thesis provides an approach to planar manipulation using soft fluidic elastomer robots. That is, novel approaches to design, fabrication, kinematic modeling, power, control, and planning as well as extensive experimental evaluations with multiple manipulator prototypes are presented. More specifically, three viable manipulator morphologies composed entirely from soft silicone rubber are explored, and these morphologies are differentiated by their actuator structures, namely: ribbed, cylindrical, and pleated. Additionally, three distinct casting-based fabrication processes are explored: lamination-based casting, retractable-pin-based casting, and lost-wax- based casting. Furthermore, two ways of fabricating a multiple DOF manipulator are explored: casting the complete manipulator as a whole, and casting single DOF segments with subsequent concatenation. An approach to closed-loop configuration control is presented using a piecewise constant curvature kinematic model, real-time localization data, and novel fluidic drive cylinders which power actuation. Multi-segment forward and inverse kinematic algorithms are developed and combined with the configuration controller to provide reliable task-space position control. Building on these developments, a suite of task-space planners are presented to demonstrate new autonomous capabilities from these soft robots such as: (i) tracking a path in free-space, (ii) maneuvering in confined environments, and (iii) grasping and placing objects. Extensive evaluations of these capabilities with physical prototypes demonstrate that manipulation with soft fluidic elastomer robots is viable. Lastly, this thesis presents a robotic manipulation system capable of autonomously positioning a multi-segment soft fluidic elastomer robot in three dimensions while subject to the self-loading effects of gravity. Specifically, an extremely soft robotic manipulator morphology that is composed entirely from low durometer elastomer, powered by pressurized air, and designed to be both modular and durable is presented. To understand the deformation of a single arm segment, a static physics-based model is developed and experimentally validated. Then, to kinematically model the multi-segment manipulator, a piece-wise constant curvature assumption consistent with more traditional continuum manipulators is used. Additionally, a complete fabrication process for this new manipulator is defined and used to make multiple functional prototypes. In order to power the robot's spatial actuation, a high capacity fluidic drive cylinder array is implemented, providing continuously variable, closed-circuit gas delivery. Next, using real-time localization data, a processing and control algorithm is developed that generates realizable kinematic curvature trajectories and controls the manipulator's configuration along these trajectories. A dynamic model for this multi-body fluidic elastomer manipulator is also developed along with a strategy for independently identifying all unknown components of the system: the soft manipulator, its distributed fluidic elastomer actuators, as well as its drive cylinders. Next, using this model and trajectory optimization techniques locally-optimal, open-loop control policies are found. Lastly, new capabilities offered by this soft fluidic elastomer manipulation system are validated with extensive physical experiments. These are: (i) entering and advancing through confined three-dimensional environments, (ii) conforming to goal shape-configurations within a sagittal plane under closed-loop control, and (iii) performing dynamic maneuvers we call grabs.
by Andrew D. Marchese.
Ph. D.
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Pan, Min, Zhe Hao, Chenggang Yuan, and Andrew Plummer. "Development and control of smart pneumatic mckibben muscles for soft robots." Technische Universität Dresden, 2020. https://tud.qucosa.de/id/qucosa%3A71262.

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Animals exploit soft structures to move smoothly and effectively in complex natural environments. These capabilities have inspired robotic engineers to incorporate soft actuating technologies into their designs. Developing soft muscle-like actuation technology is one of the grand challenges in the creation of soft-body robots that can move, deform their body, and modulate body stiffness. This paper presents the development of smart pneumatic McKibben muscles woven and reinforced by using conductive insulated wires to equip the muscles with an inherent sensing capability, in which the deformation of the muscles can be effectively measured by calculating the change of wire inductance. Sensing performance of a variety of weaving angles is investigated. The ideal McKibben muscle models are used for analysing muscle performance and sensing accuracy. The experimental results show that the contraction of the muscles is proportional to the measured change of inductance. This relationship is applied to a PID control system to control the contraction of smart muscles in simulation, and good control performance is achieved. The creation of smart muscles with an inherent sensing capability and a good controllability is promising for operation of future soft robots.
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Zhang, Zhongkai. "Vision-based calibration, position control and force sensing for soft robots." Thesis, Lille 1, 2019. http://www.theses.fr/2019LIL1I001/document.

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La modélisation de robots souples est extrêmement difficile, à cause notamment du nombre théoriquement infini des degrés de liberté. Cette difficulté est accentuée lorsque les robots ont des configurations complexes. Ce problème de modélisation entraîne de nouveaux défis pour la calibration et la conception des commandes des robots, mais également de nouvelles opportunités avec de nouvelles stratégies de détection de force possibles. Cette thèse a pour objectif de proposer des solutions nouvelles et générales utilisant la modélisation et la vision. La thèse présente dans un premier temps un modèle cinématique à temps discret pour les robots souples reposant sur la méthode des éléments finis (FEM) en temps réel. Ensuite, une méthode de calibration basée sur la vision du système de capteur-robot et des actionneurs est étudiée. Deux contrôleurs de position en boucle fermée sont conçus. En outre, pour traiter le problème de la perte d'image, une stratégie de commande commutable est proposée en combinant à la fois le contrôleur à boucle ouverte et le contrôleur à boucle fermée. Deux méthodes (avec et sans marqueur(s)) de détection de force externe pour les robots déformables sont proposées. L'approche est basée sur la fusion de mesures basées sur la vision et le modèle par FEM. En utilisant les deux méthodes, il est possible d'estimer non seulement les intensités, mais également l'emplacement des forces externes. Enfin, nous proposons une application concrète : un robot cathéter dont la flexion à l'extrémité est piloté par des câbles. Le robot est contrôlé par une stratégie de contrôle découplée qui permet de contrôler l’insertion et la flexion indépendamment, tout en se basant sur un modèle FEM
The modeling of soft robots which have, theoretically, infinite degrees of freedom, are extremely difficult especially when the robots have complex configurations. This difficulty of modeling leads to new challenges for the calibration and the control design of the robots, but also new opportunities with possible new force sensing strategies. This dissertation aims to provide new and general solutions using modeling and vision. The thesis at first presents a discrete-time kinematic model for soft robots based on the real-time Finite Element (FE) method. Then, a vision-based simultaneous calibration of sensor-robot system and actuators is investigated. Two closed-loop position controllers are designed. Besides, to deal with the problem of image feature loss, a switched control strategy is proposed by combining both the open-loop controller and the closed-loop controller. Using soft robot itself as a force sensor is available due to the deformable feature of soft structures. Two methods (marker-based and marker-free) of external force sensing for soft robots are proposed based on the fusion of vision-based measurements and FE model. Using both methods, not only the intensities but also the locations of the external forces can be estimated.As a specific application, a cable-driven continuum catheter robot through contacts is modeled based on FE method. Then, the robot is controlled by a decoupled control strategy which allows to control insertion and bending independently. Both the control inputs and the contact forces along the entire catheter can be computed by solving a quadratic programming (QP) problem with a linear complementarity constraint (QPCC)
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Gaskett, Chris. "Q-Learning for robot control." View thesis entry in Australian Digital Theses Program, 2002. http://eprints.jcu.edu.au/623/1/gaskettthesis.pdf.

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Q-Learning is a method for solving reinforcement learning problems. Reinforcement learning problems require improvement of behaviour based on received rewards. Q-Learning has the potential to reduce robot programming effort and increase the range of robot abilities. However, most currentQ-learning systems are not suitable for robotics problems: they treat continuous variables, for example speeds or positions, as discretised values. Discretisation does not allow smooth control and does not fully exploit sensed information. A practical algorithm must also cope with real-time constraints, sensing and actuation delays, and incorrect sensor data. This research describes an algorithm that deals with continuous state and action variables without discretising. The algorithm is evaluated with vision-based mobile robot and active head gaze control tasks. As well as learning the basic control tasks, the algorithm learns to compensate for delays in sensing and actuation by predicting the behaviour of its environment. Although the learned dynamic model is implicit in the controller, it is possible to extract some aspects of the model. The extracted models are compared to theoretically derived models of environment behaviour. The difficulty of working with robots motivates development of methods that reduce experimentation time. This research exploits Q-learning’s ability to learn by passively observing the robot’s actions—rather than necessarily controlling the robot. This is a valuable tool for shortening the duration of learning experiments.
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Hyatt, Phillip Edmond. "Robust Real-Time Model Predictive Control for High Degree of Freedom Soft Robots." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8453.

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This dissertation is focused on the modeling and robust model-based control of high degree-of-freedom (DoF) systems. While most of the contributions are applicable to any difficult-to-model system, this dissertation focuses specifically on applications to large-scale soft robots because their many joints and pressures constitute a high-DoF system and their inherit softness makes them difficult to model accurately. First a joint-angle estimation and kinematic calibration method for soft robots is developed which is shown to decrease the pose prediction error at the end of a 1.5 m robot arm by about 85\%. A novel dynamic modelling approach which can be evaluated within microseconds is then formulated for continuum type soft robots. We show that deep neural networks (DNNs) can be used to approximate soft robot dynamics given training examples from physics-based models like the ones described above. We demonstrate how these machine-learning-based models can be evaluated quickly to perform a form of optimal control called model predictive control (MPC). We describe a method of control trajectory parameterization that enables MPC to be applied to systems with more DoF and with longer prediction horizons than previously possible. We show that this parameterization decreases MPC's sensitivity to model error and drastically reduces MPC solve times. A novel form of MPC is developed based on an evolutionary optimization algorithm that allows the optimization to be parallelized on a computer's graphics processing unit (GPU). We show that this evolutionary MPC (EMPC) can greatly decrease MPC solve times for high DoF systems without large performance losses, especially given a large GPU. We combine the ideas of machine learned DNN models of robot dynamics, with parameterized and parallelized MPC to obtain a nonlinear version of EMPC which can be run at higher rates and find better solutions than many state-of-the-art optimal control methods. Finally we demonstrate an adaptive form of MPC that can compensate for model error or changes in the system to be controlled. This adaptive form of MPC is shown to inherit MPC's robustness to completely unmodeled disturbances and adaptive control's ability to decrease trajectory tracking errors over time.
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Mirano, Geronimo (Geronimo J. ). "Jacobian-based control of soft robots for manipulation using implicit surface models." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113126.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 47).
Soft robot hands offer numerous advantages over rigid ones for manipulation, including robustness and safety. Yet, compared to rigid robots, soft robots are characterized by continuous mechanics, and finite-element approximations with many degrees of freedom present a significant obstacle for modern control approaches. The central question my thesis explores is whether we can capture the benefits of soft robot hands with relatively simple dynamical models. Specifically, we demonstrate a very simple model of a 2D soft manipulator that uses pulleys and cables to model deformable surfaces. This model captures much of the qualitative behavior of soft membranes, while also proving amenable to modern control techniques. We validate this model physically using a hardware set-up. We then demonstrate a simple quasi-static Jacobian controller which solves a second-order cone program to achieve the task of in-hand object repositioning.
by Geronimo Mirano.
M. Eng.
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Yang, Hee Doo. "Design, Manufacturing, and Control of Soft and Soft/Rigid Hybrid Pneumatic Robotic Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/100635.

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Soft robotic systems have recently been considered as a new approach that is in principle better suited for tasks where safety and adaptability are important. That is because soft materials are inherently compliant and resilient in the event of collisions. They are also lightweight and can be low-cost; in general, soft robots have the potential to achieve many tasks that were not previously possible with traditional robotic systems. In this paper, we propose a new manufacturing process for creating multi-chambered pneumatic actuators and robots. We focus on using fabric as the primary structural material, but plastic films can be used instead of textiles as well. We introduce two different methods to create layered bellows actuators, which can be made with a heat press machine or in an oven. We also describe origami-like actuators with possible corner structures. Moreover, the fabrication process permits the creation of soft and soft/rigid hybrid robotic systems, and enables the easy integration of sensors into these robots. We analyze various textiles that are possibly used with this method, and model bellows actuators including operating force, restoring force, and estimated geometry with multiple bellows. We then demonstrate the process by showing a bellows actuator with an embedded sensor and other fabricated structures and robots. We next present a new design of a multi-DOF soft/rigid hybrid robotic manipulator. It contains a revolute actuator and several roll-pitch actuators which are arranged in series. To control the manipulator, we use a new variant of the piece-wise constant curvature (PCC) model. The robot can be controlled using forward and inverse kinematics with embedded inertial measurement units (IMUs). A bellows actuator, which is a subcomponent of the manipulator, is modeled with a variable-stiffness spring, and we use the model to predict the behavior of the actuator. With the model, the roll-pitch actuator stiffnesses are measured in all directions through applying forces and torques. The stiffness is used to predict the behavior of the end effector. The robotic system introduced achieved errors of less than 5% when compared to the models, and positioning accuracies of better than 1cm.
Doctor of Philosophy
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Books on the topic "Control and learning of soft robots"

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Stefan, Wermter, Palm Günther, and Elshaw Mark, eds. Biomimetic neural learning for intelligent robots: Intelligent systems, cognitive robotics, intelligent robots. Berlin: Springer, 2005.

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Design and control of intelligent robotic systems. Berlin: Springer, 2009.

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Towards real learning robots. Frankfurt am Main: Peter Lang, 2000.

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Towards real learning robots. Frankfurt am Main: Peter Lang, 1999.

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de, Velde Walter Van, ed. Toward learning robots. Cambridge, Mass: MIT Press, 1993.

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Crangle, Colleen. Language and learning for robots. Stanford, Calif: Center for the Study of Language and Information, 1994.

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Inoue, Takahiro. Mechanics and control of soft-fingered manipulation. London: Springer, 2009.

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Inoue, Takahiro. Mechanics and control of soft-fingered manipulation. London: Springer, 2009.

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Inoue, Takahiro. Mechanics and control of soft-fingered manipulation. London: Springer, 2009.

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1963-, Hirai Shinʼichi, ed. Mechanics and control of soft-fingered manipulation. London: Springer, 2009.

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Book chapters on the topic "Control and learning of soft robots"

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Zhang, Haochong, Rongyun Cao, Shlomo Zilberstein, Feng Wu, and Xiaoping Chen. "Toward Effective Soft Robot Control via Reinforcement Learning." In Intelligent Robotics and Applications, 173–84. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65289-4_17.

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Lee, Wei-Po, and Tsung-Hsien Yang. "Learning RNN-Based Gene Regulatory Networks for Robot Control." In Advances in Intelligent and Soft Computing, 93–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03156-4_10.

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Rafajłowicz, Ewaryst, and Wojciech Rafajłowicz. "Iterative Learning of Optimal Control – Case Study of the Gantry Robot." In Artificial Intelligence and Soft Computing, 337–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59060-8_30.

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Fujita, Hamido, and Yu-Chien Ko. "Subjective Weights Based Meta-Learning in Multi-criteria Decision Making." In Advances in Soft Computing, Intelligent Robotics and Control, 109–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05945-7_7.

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Tushkanov, Nikolay, Vladimir Nazarov, Alla Kuznetsova, and Olga Tushkanova. "Multi-sensor System of Intellectual Handling Robot Control on the Basis of Collective Learning Paradigm." In Advances in Intelligent and Soft Computing, 195–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25661-5_26.

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Zhang, Yunce, Tao Wang, Ning Tan, and Shiqiang Zhu. "Open-Loop Motion Control of a Hydraulic Soft Robotic Arm Using Deep Reinforcement Learning." In Intelligent Robotics and Applications, 302–12. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89095-7_30.

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Haddadin, Sami. "Soft-Robotics Control." In Towards Safe Robots, 25–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40308-8_3.

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Accame, M. "Learning to Control a Visual Sensing System." In Making Robots Smarter, 109–25. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5239-0_7.

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Grube, Malte, and Robert Seifried. "An Optical Curvature Sensor for Soft Robots." In ROMANSY 24 - Robot Design, Dynamics and Control, 125–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06409-8_13.

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Li, Mengdan, Yu Huo, Gong Wang, Yifei Liu, and Bingshan Liu. "Soft Variable Structure Control in Flexible-Joint Robots." In Advances in Intelligent Systems and Computing, 801–7. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0238-5_84.

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Conference papers on the topic "Control and learning of soft robots"

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Rajendran, Sunil Kumar, and Feitian Zhang. "Learning Based Speed Control of Soft Robotic Fish." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-8977.

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Bioinspired robotics takes advantage of biological systems in nature for morphology, action and perception to build advanced robots of compelling performance and wide application. This paper focuses on the design, modeling and control of a bioinspired robotic fish. The design utilizes a recently-developed artificial muscle named super coiled polymer for actuation and a soft material (silicone rubber) for building the robot body. The paper proposes a learning based speed control design approach for bioinspired robotic fish using model-free reinforcement learning. Based on a mathematically tractable dynamic model derived by approximating the robotic fish with a three-link robot, speed control simulation is conducted to demonstrate and validate the control design method. Exampled with a three-link reduced-order dynamic system, the proposed learning based control design approach is applicable to many and various complicated bioinspired robotic systems.
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Zhao, Leidi, Raheem Lawhorn, Siddharth Patil, Steve Susanibar, Lu Lu, Cong Wang, and Bo Ouyang. "Multiform Adaptive Robot Skill Learning From Humans." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5114.

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Object manipulation is a basic element in everyday human lives. Robotic manipulation has progressed from maneuvering single-rigid-body objects with firm grasping to maneuvering soft objects and handling contact-rich actions. Meanwhile, technologies such as robot learning from demonstration have enabled humans to intuitively train robots. This paper discusses a new level of robotic learning-based manipulation. In contrast to the single form of learning from demonstration, we propose a multiform learning approach that integrates additional forms of skill acquisition, including adaptive learning from definition and evaluation. Moreover, going beyond state-of-the-art technologies of handling purely rigid or soft objects in a pseudo-static manner, our work allows robots to learn to handle partly rigid partly soft objects with time-critical skills and sophisticated contact control. Such capability of robotic manipulation offers a variety of new possibilities in human-robot interaction.
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Pawlowski, Ben, Charles W. Anderson, and Jianguo Zhao. "Dynamic Control of Soft Robots Using Reinforcement Learning." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9181.

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Abstract Soft robots made from soft materials recently attracted tremendous research owing to their unique softness compared with rigid robots, making them suitable for applications such as manipulation and locomotion. However, also due to their softness, the modeling and control of soft robots present a significant challenge because of the infinite degree of freedom. In this case, although analytic solutions can be derived for control, they are too computationally intensive for real-time application. In this paper, we aim to leverage reinforcement learning to approach the control problem. We gradually increase the complexity of the control problems to learn. We also test the effectiveness and efficiency of reinforcement learning techniques to the control of soft robots for different tasks. Simulation results show that the control commands to be computed in milliseconds, allowing effective control of soft manipulators, up to trajectory tracking.
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Modugno, Valerio, Gerard Neumann, Elmar Rueckert, Giuseppe Oriolo, Jan Peters, and Serena Ivaldi. "Learning soft task priorities for control of redundant robots." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487137.

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Luo, Ming, Mahdi Agheli, and Cagdas D. Onal. "Theoretical Modeling of a Pressure-Operated Soft Snake Robot." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35340.

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This paper addresses the theoretical modeling of the dynamics of a pressure-operated soft snake robot. An accurate dynamic model is a fundamental requirement for optimization, control, navigation, and learning algorithms for a mobile robot that can undergo serpentine locomotion. Such algorithms can be readily implemented for traditional rigid robots, but remain a challenge for nonlinear and low-bandwidth soft robotic systems. A framework to solve the 2-D modeling problem of a soft robotic snake is detailed with a general approach applicable to most pressure-operated soft robots that are developed by a modular kinematic arrangement of bending-type fluidic elastomer actuators. The model is simulated using measured physical parameters of the robot and workspace. The theoretical results are verified through a proof-of-concept comparison to locomotion experiments on a flat surface with measured frictional properties. Experimental results indicate that the proposed model describes the motion of the robot.
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You, Xuanke, Yixiao Zhang, Xiaotong Chen, Xinghua Liu, Zhanchi Wang, Hao Jiang, and Xiaoping Chen. "Model-free control for soft manipulators based on reinforcement learning." In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017. http://dx.doi.org/10.1109/iros.2017.8206123.

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Gillespie, Morgan T., Charles M. Best, Eric C. Townsend, David Wingate, and Marc D. Killpack. "Learning nonlinear dynamic models of soft robots for model predictive control with neural networks." In 2018 IEEE International Conference on Soft Robotics (RoboSoft). IEEE, 2018. http://dx.doi.org/10.1109/robosoft.2018.8404894.

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Li, Yingqi, Xiaomei Wang, and Ka-Wai Kwok. "Towards Adaptive Continuous Control of Soft Robotic Manipulator using Reinforcement Learning." In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9981335.

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Wang, Xinran, and Nicolas Rojas. "A Data-Efficient Model-Based Learning Framework for the Closed-Loop Control of Continuum Robots." In 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft). IEEE, 2022. http://dx.doi.org/10.1109/robosoft54090.2022.9762115.

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Yan, Changzhi, Qiyuan Zhang, Zhaoyang Liu, Xueqian Wang, and Bin Liang. "Control of Free-Floating Space Robots to Capture Targets Using Soft Q-Learning." In 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2018. http://dx.doi.org/10.1109/robio.2018.8665049.

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Reports on the topic "Control and learning of soft robots"

1

Abdula, Andrii I., Halyna A. Baluta, Nadiia P. Kozachenko, and Darja A. Kassim. Peculiarities of using of the Moodle test tools in philosophy teaching. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3867.

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The paper considers the role of philosophy and philosophical disciplines as the means of forming general cultural competences, in particular, in the development of critical thinking. The article emphasizes that the process of forming over-subject and soft skills, which, as a rule, include also critical thinking, gets much more complicated under the conditions of the reduction in the volume of philosophical courses. The paper grounds that one of the ways to “return” philosophy to educational programmes can be the implementation of training, using the e-learning environment, especially Moodle. In addition, authors point to the expediency of using this system and, in general, e-learning as an instrument for collaborating students to the world’s educational community and for developing their lifelong learning skills. The article specifies the features of providing electronic support in philosophy teaching, to which the following belongs: the difficulty of parametrizing the learning outcomes; plurality of approaches; communicative philosophy. The paper highlights the types of activities that can be implemented by tools of Moodle. The use of the following Moodle test tasks is considered as an example: test control in the flipped class, control of work with primary sources, control of self-study, test implementation of interim thematic control. The authors conclude that the Moodle system can be used as a tools of online support for the philosophy course, but it is impossible to transfer to the virtual space all the study of this discipline, because it has a significant worldview load. Forms of training, directly related to communication, are integral part of the methodology of teaching philosophy as philosophy itself is discursive, dialogical, communicative and pluralistic. Nevertheless, taking into account features of the discipline, it is possible to provide not only the evaluation function of the test control, but also to realize a number of educational functions: updating the basic knowledge, memorization, activating the cognitive interest, developing the ability to reason and the simpler ones but not less important, – the skill of getting information and familiarization with it.
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