Field Of Robotics Research Articles

Bioinspired Design and Experimental Validation of an Aquatic Snake Robot.

This article presents the design, simulation, and experimental validation of a novel modular aquatic snake robot capable of surface locomotion. The modular structure allows each unit to function independently, facilitating ease of maintenance and adaptability to diverse aquatic environments. Employing the material point method with the moving least squares (MPM-MLS) simulation technique, the robot's dynamic behavior was analyzed, yielding reliable results. The control algorithm, integral to the robot's autonomous navigation, was implemented to enable forward propulsion at high speed, steering, and obstacle detection and avoidance. Extensive testing of the aquatic snake robot was conducted, demonstrating its practical viability. The robot showcased promising swimming capabilities, achieving high speeds and maneuverability. Furthermore, the obstacle detection and avoidance mechanisms were proven effective, showing the robot's ability to navigate through dynamic environments. The presented aquatic snake robot represents an advancement in the field of underwater robotics, offering a modular and versatile solution for tasks ranging from environmental monitoring to search and rescue operations.

Design and implementation of a field robot using a parallelogram mechanism.

To meet various industrial requirements such as ease of motion, scalability, and cost efficiency, it is necessary to innovate the design of robotic platforms. In this research, a novel approach, from mechanical design to control implementation, is introduced for launching a robotic platform using a parallelogram mechanism. First, a reverse engineering process is applied, progressing from kinematics to dynamics. Then, several mechanical computations are conducted to ensure the structural stability of the robot framework. Subsequently, the dynamic performance of the system is analyzed, focusing on the driving torque and moments in each link. Additionally, the electrical design and transfer function of each joint are identified to ensure practical execution. To validate the effectiveness and feasibility of the design, both numerical simulations and experimental tests are performed. Theoretical results show the dynamic response of the proposed method, particularly in terms of the driving moments of the robotic joints. In real-world tests, various trajectories, such as different rectangular paths, are demonstrated to showcase the robot's capabilities. From these results, it is clear that the proposed approach is both feasible and applicable in practical scenarios.

Research on space attitude recognition and control method for soft robotics based on neural networks

In the modern field of biomedical robotics, there is increasing attention on the use of soft robots as carriers for surgical devices. Compared to rigid robots, soft robots exhibit more complex postures and work environments, requiring new methods for recognizing and controlling their postures within biological organisms. This paper investigates the recognition and control of the posture of soft robots using a neural network learning approach, utilizing a soft robot equipped with resistive sensors. By establishing the relationship between changes in resistance values and posture variations, the study successfully achieves the identification and control of the soft robot's posture. The obtained posture data based on resistance values are validated for reliability. Thus, by combining resistive sensors with soft robots and employing neural network analysis, the recognition and control of soft robots postures within biological organisms can be achieved.

TRENDS IN STUDIES ON EDUCATIONAL ROBOTICS IN RECENT YEARS: A CONTENT ANALYSIS

This study aims to analyze the general trends in educational robotics in recent years. The document analysis method was preferred in this research. Accordingly, the content of 100 articles published in the field of educational robotics in recent years was analyzed in terms of methodology, sample characteristics, robotic tools used, commonly covered topics, and findings. The study revealed that Lego Mindstorms, Arduino, Python, and Scratch are the most commonly used robotic tools in educational robotics. It was observed that students predominantly participated in the studies, with a focus on university and middle school levels. Moreover, the analysis indicated that quantitative studies mostly employed scales and questionnaires, whereas qualitative studies frequently used interviews and observation forms as measurement tools. The most frequently examined variables in these studies were computational thinking, problem-solving, creativity, academic achievement, collaboration, and motivation. While the findings generally emphasized the positive effects of educational robotics and its significance for integration into other domains, the challenges of educators’ knowledge and experience in robotics, infrastructure and technical issues, and difficulties in practical implementation were also identified as disadvantages.

An Origami Continuum Manipulator with Modularized Design and Hybrid Actuation: Accurate Kinematic Modeling and Experiments

Herein, this study contributes significantly to the advancement of continuum manipulators in two main aspects. First, a modularization concept and a hybrid actuation scheme to create a novel origami continuum manipulator with exceptional deformability are introduced. Second, an accurate model and framework for the forward and inverse kinematic analysis of origami manipulators are proposed. Specifically, each origami manipulator module can achieve axial extension and bending deformation by coordinated actuation of shape memory alloy (SMA) and pneumatic muscles, and the manipulator's end is equipped with a deformable gripper based on waterbomb origami and actuated by SMA. Through careful consideration of the self‐weight and torque balance, an accurate kinematic model based on the Denavit–Hartenberg method is established, which enables one to effectively predict the reachable extreme positions and spatial poses of the manipulator and solve the inverse kinematics using a genetic algorithm. Comprehensive experiments are conducted to validate the design's rationality and model's accuracy . In these tests, the rich spatial configurations are not only demonstrated that can be achieved by integrating hybrid actuators with origami modules but also the accuracy and reliability of the kinematic model are confirmed, opening up possibilities for the advancement and application of origami‐inspired robotics in various fields.

Understanding Human Dynamic Sampling Objectives to Enable Robot-assisted Scientific Decision Making

Truly collaborative scientific field data collection between human scientists and autonomous robot systems requires a shared understanding of the search objectives and tradeoffs faced when making decisions. Therefore, critical to developing intelligent robots to aid human experts is an understanding of how scientists make such decisions and how they adapt their data collection strategies when presented with new information in situ . In this study, we examined the dynamic data collection decisions of 108 expert geoscience researchers using a simulated field scenario. Human data collection behaviors suggested two distinct objectives: an information-based objective to maximize information coverage and a discrepancy-based objective to maximize hypothesis verification. We developed a highly simplified quantitative decision model that allows the robot to predict potential human data collection locations based on the two observed human data collection objectives. Predictions from the simple model revealed a transition from information-based to discrepancy-based objective as the level of information increased. The findings will allow robotic teammates to connect experts’ dynamic science objectives with the adaptation of their sampling behaviors and, in the long term, enable the development of more cognitively compatible robotic field assistants.

Design, optimization, debugging, and simulation of an agricultural biomimetic hexapod robot

This paper comprehensively studies the model establishment, kinematic analysis, hardware design, software development, and agricultural applications of hexapod robot, laying a solid foundation for their practical application in the agricultural field and proposing prospects for future research and development. This article mainly conducts in-depth research on the mechanical design, kinematic analysis, hardware implementation, software development, and application in the agricultural field of hexapod robots. Firstly, a brief introduction to biomimetic robots will be provided, and a comparative analysis will be conducted on existing common legged robots. Afterwards, use solidworks-2021 for 3D modeling to draw the legs and main structure of the robot. In terms of kinematic analysis, by studying the mechanism structure and motion characteristics of hexapod robot, the forward and inverse kinematic equations are derived, and the gait planning and motion control algorithms of the robot are analyzed in detail. These analyses provide a theoretical basis for the robot's program and actual motion. In terms of hardware, a lightweight and high-performance hexapod robot hardware platform has been designed using mainstream controllers such as Borboardino and ultrasonic sensors. This hardware platform has excellent responsiveness and adaptability, and can efficiently move and operate in complex agricultural environments. In terms of software, a comprehensive control system has been developed, including motion planning, action execution, and obstacle avoidance modules. By integrating triangular gait technology and related algorithms, robot can achieve navigation and obstacle avoidance, and perform tasks such as crop planting and spraying in the agricultural field. The simulation file of the hexapod robot was established using CoppeliaSim software, achieving motion based on the robot body, six legs, and end effectors. Finally, a 3D-printer is used to print and assemble all components, and complete the design through debugging and verification. The hexapod robot is capable of autonomous cruising in farmland, completing tasks such as planting and watering crops, providing efficient and intelligent agricultural production solutions for farmers.

Nonlinear dynamics of a circular curved cantilevered pipe conveying pulsating fluid based on the geometrically exact model

Due to the novel applications of flexible pipes conveying fluid in the field of soft robotics and biomedicine, the investigations on the mechanical responses of the pipes have attracted considerable attention. The fluid-structure interaction (FSI) between the pipe with a curved shape and the time-varying internal fluid flow brings a great challenge to the revelation of the dynamical behaviors of flexible pipes, especially when the pipe is highly flexible and usually undergoes large deformations. In this work, the geometrically exact model (GEM) for a curved cantilevered pipe conveying pulsating fluid is developed based on the extended Hamilton’s principle. The stability of the curved pipe with three different subtended angles is examined with the consideration of steady fluid flow. Specific attention is concentrated on the large-deformation resonance of circular pipes conveying pulsating fluid, which is often encountered in practical engineering. By constructing bifurcation diagrams, oscillating shapes, phase portraits, time traces, and Poincare maps, the dynamic responses of the curved pipe under various system parameters are revealed. The mean flow velocity of the pulsating fluid is chosen to be either subcritical or supercritical. The numerical results show that the curved pipe conveying pulsating fluid can exhibit rich dynamical behaviors, including periodic and quasi-periodic motions. It is also found that the preferred instability type of a cantilevered curved pipe conveying steady fluid is mainly in the flutter of the second mode. For a moderate value of the mass ratio, however, a third-mode flutter may occur, which is quite different from that of a straight pipe system.

SyS3DS: Systematic Sampling of Large-Scale LiDAR Point Clouds for Semantic Segmentation in Forestry Robotics

Recently, new semantic segmentation and object detection methods have been proposed for the direct processing of three-dimensional (3D) LiDAR sensor point clouds. LiDAR can produce highly accurate and detailed 3D maps of natural and man-made environments and is used for sensing in many contexts due to its ability to capture more information, its robustness to dynamic changes in the environment compared to an RGB camera, and its cost, which has decreased in recent years and which is an important factor for many application scenarios. The challenge with high-resolution 3D LiDAR sensors is that they can output large amounts of 3D data with up to a few million points per second, which is difficult to process in real time when applying complex algorithms and models for efficient semantic segmentation. Most existing approaches are either only suitable for relatively small point clouds or rely on computationally intensive sampling techniques to reduce their size. As a result, most of these methods do not work in real time in realistic field robotics application scenarios, making them unsuitable for practical applications. Systematic point selection is a possible solution to reduce the amount of data to be processed. Although our approach is memory and computationally efficient, it selects only a small subset of points, which may result in important features being missed. To address this problem, our proposed systematic sampling method called SyS3DS (Systematic Sampling for 3D Semantic Segmentation) incorporates a technique in which the local neighbours of each point are retained to preserve geometric details. SyS3DS is based on the graph colouring algorithm and ensures that the selected points are non-adjacent in order to obtain a subset of points that are representative of the 3D points in the scene. To take advantage of the ensemble learning method, we pass a different subset of nodes for each epoch. This leverages a new technique called auto-ensemble, where ensemble learning is proposed as a collection of different learning models instead of tuning different hyperparameters individually during training and validation. SyS3DS has been shown to process up to 1 million points in a single pass. It outperforms the state of the art in efficient semantic segmentation on large datasets such as Semantic3D. We also present a preliminary study on the validity of the performance of LiDAR-only data, i.e., intensity values from LiDAR sensors without RGB values for semi-autonomous robot perception.

A dynamic reward-enhanced Q-learning approach for efficient path planning and obstacle avoidance in mobile robotics

PurposeThe purpose of the paper is to propose and demonstrate a novel approach for addressing the challenges of path planning and obstacle avoidance in the context of mobile robots (MR). The specific objectives and purposes outlined in the paper include: introducing a new methodology that combines Q-learning with dynamic reward to improve the efficiency of path planning and obstacle avoidance. Enhancing the navigation of MR through unfamiliar environments by reducing blind exploration and accelerating the convergence to optimal solutions and demonstrating through simulation results that the proposed method, dynamic reward-enhanced Q-learning (DRQL), outperforms existing approaches in terms of achieving convergence to an optimal action strategy more efficiently, requiring less time and improving path exploration with fewer steps and higher average rewards.Design/methodology/approachThe design adopted in this paper to achieve its purposes involves the following key components: (1) Combination of Q-learning and dynamic reward: the paper’s design integrates Q-learning, a popular reinforcement learning technique, with dynamic reward mechanisms. This combination forms the foundation of the approach. Q-learning is used to learn and update the robot’s action-value function, while dynamic rewards are introduced to guide the robot’s actions effectively. (2) Data accumulation during navigation: when a MR navigates through an unfamiliar environment, it accumulates experience data. This data collection is a crucial part of the design, as it enables the robot to learn from its interactions with the environment. (3) Dynamic reward integration: dynamic reward mechanisms are integrated into the Q-learning process. These mechanisms provide feedback to the robot based on its actions, guiding it to make decisions that lead to better outcomes. Dynamic rewards help reduce blind exploration, which can be time-consuming and inefficient and promote faster convergence to optimal solutions. (4) Simulation-based evaluation: to assess the effectiveness of the proposed approach, the design includes a simulation-based evaluation. This evaluation uses simulated environments and scenarios to test the performance of the DRQL method. (5) Performance metrics: the design incorporates performance metrics to measure the success of the approach. These metrics likely include measures of convergence speed, exploration efficiency, the number of steps taken and the average rewards obtained during the robot’s navigation.FindingsThe findings of the paper can be summarized as follows: (1) Efficient path planning and obstacle avoidance: the paper’s proposed approach, DRQL, leads to more efficient path planning and obstacle avoidance for MR. This is achieved through the combination of Q-learning and dynamic reward mechanisms, which guide the robot’s actions effectively. (2) Faster convergence to optimal solutions: DRQL accelerates the convergence of the MR to optimal action strategies. Dynamic rewards help reduce the need for blind exploration, which typically consumes time and this results in a quicker attainment of optimal solutions. (3) Reduced exploration time: the integration of dynamic reward mechanisms significantly reduces the time required for exploration during navigation. This reduction in exploration time contributes to more efficient and quicker path planning. (4) Improved path exploration: the results from the simulations indicate that the DRQL method leads to improved path exploration in unknown environments. The robot takes fewer steps to reach its destination, which is a crucial indicator of efficiency. (5) Higher average rewards: the paper’s findings reveal that MR using DRQL receive higher average rewards during their navigation. This suggests that the proposed approach results in better decision-making and more successful navigation.Originality/valueThe paper’s originality stems from its unique combination of Q-learning and dynamic rewards, its focus on efficiency and speed in MR navigation and its ability to enhance path exploration and average rewards. These original contributions have the potential to advance the field of mobile robotics by addressing critical challenges in path planning and obstacle avoidance.

Evaluating Agriculture Robots: Case Study and Analysis

The technical revolution paved the way for the development of robots that can now operate in a variety of areas. The smart farming technique called robotic agrifarming has the potential to alleviate the worldwide labor shortage while increasing agricultural productivity and food safety. From planting to harvesting, agribots perform field operations that enhance soil quality and ensure long-term growth. That's why it's essential to create a decision-support system that helps choose the best robot for an agricultural task. The goal of this study is to create an integrated decision system for assessing agriculture robots using the method based on removal effects of criteria (MEREC). Founded on a lot of alternatives like project viability, ecosystem aptness, community conditions, governmental embracing, and profit, the performance of seven field robots is evaluated. The advantage of the developed ranking technique is that it follows the additive ratio assessment (ARAS) method, which is indeed a specific technique used for multi-attribute decision-making (MADM), particularly in the field of environmental management and evaluation. It's commonly employed when there are multiple factors or criteria to consider in decision-making processes and prioritizing the alternatives.

An Untethered Soft Crawling Robot Driven by Wireless Power Transfer Technology.

Soft robots based on flexible materials have attracted the attention due to high flexibility and great environmental adaptability. Among the common driving modes, electricity, light, and magnetism have the limitations of wiring, poor penetration capability, and sophisticated equipment, respectively. Here, an emerging wireless driving mode is proposed for the soft crawling robot based on wireless power transfer (WPT) technology. The receiving coil at the robot's tail, as an energy transfer station, receives energy from the transmitting coil and supplies the electrothermal responsiveness to drive the robot's crawling. By regulating the WPT's duration to control the friction between the robot and the ground, bidirectional crawling is realized. Furthermore, the receiving coil is also employed as a sensory organ to equip the robot with localization, ID recognition, and sensing capabilities based on electromagnetic coupling. This work provides an innovative and promising strategy for the design and integration of soft crawling robots, exhibiting great potential in the field of intelligent robots.

A 3D-MICROPRINTED COAXIAL NOZZLE FOR FABRICATING LONG, FLEXIBLE MICROFLUIDIC TUBING.

A variety of emerging applications, particularly those in medical and soft robotics fields, are predicated on the ability to fabricate long, flexible meso/microfluidic tubing with high customization. To address this need, here we present a hybrid additive manufacturing (or "three-dimensional (3D) printing") strategy that involves three key steps: (i) using the "Vat Photopolymerization (VPP) technique, "Liquid-Crystal Display (LCD)" 3D printing to print a bulk microfluidic device with three inlets and three concentric outlets; (ii) using "Two-Photon Direct Laser Writing (DLW)" to 3D microprint a coaxial nozzle directly atop the concentric outlets of the bulk microdevice, and then (iii) extruding paraffin oil and a liquid-phase photocurable resin through the coaxial nozzle and into a polydimethylsiloxane (PDMS) channel for UV exposure, ultimately producing the desired tubing. In addition to fabricating the resulting tubing-composed of polymerized photomaterial-at arbitrary lengths (e.g., > 10 cm), the distinct input pressures can be adjusted to tune the inner diameter (ID) and outer diameter (OD) of the fabricated tubing. For example, experimental results revealed that increasing the driving pressure of the liquid-phase photomaterial from 50 kPa to 100 kPa led to fluidic tubing with IDs and ODs of 291±99 μm and 546±76 μm up to 741±31 μm and 888±39 μm, respectively. Furthermore, preliminary results for DLW-printing a microfluidic "M" structure directly atop the tubing suggest that the tubing could be used for "ex situ DLW (esDLW)" fabrication, which would further enhance the utility of the tubing.

Overview of Pipeline Inspection Robots

Pipelines are widely used in our daily life and industrial production, in order to solve the ensuring problem of pipeline failure, many kinds of pipeline inspection robots have appeared, and nowadays, pipeline robots have been widely used in various pipeline inspection and repair. This paper mainly discusses the importance of pipeline inspection robots in various pipeline fields, describes the technical characteristics of pipeline robots at home and abroad in recent years, and puts forward suggestions for the future development of pipeline robots in view of the existing problems, which is of reference significance for the future development.

Robot manipulator tracking control with conquering joint physical limits at angle and velocity layers based on different-layer equivalency technique

The study of manipulator physical limits is a very important direction in the field of robotics. Most traditional approaches to addressing physical limits are typically within the framework of optimization, treating physical limits as constraints to be addressed. However, this approach has a limitation in that it may not be real-time responsive. In order to improve real-time performance, latest study in literature formulated physical limits and tracking task as different-layer time-varying problems. This approach effectively enhances real-time performance via obtaining calculated control variables in advance. Nevertheless, this study only considers joint angle limits, without taking into account the higher-layer joint velocity limits. This work conquers physical limits at angle and velocity layers simultaneously by a three-layer time-varying system. Two different ways are used to solve the system based on different-layer equivalency technique, and two differential equations are obtained. The equations are discretized by using a discretization formula, and then two discrete models are proposed. The robot tracking control conquers physical limits at angle and velocity layers simultaneously by using discrete models. Theoretical convergence of the discrete models is demonstrated. Furthermore, the effectiveness and superiority of models are also verified through numerical experiments.

Soft touchless sensors and touchless sensing for soft robots.

Soft robots are characterized by their mechanical compliance, making them well-suited for various bio-inspired applications. However, the challenge of preserving their flexibility during deployment has necessitated using soft sensors which can enhance their mobility, energy efficiency, and spatial adaptability. Through emulating the structure, strategies, and working principles of human senses, soft robots can detect stimuli without direct contact with soft touchless sensors and tactile stimuli. This has resulted in noteworthy progress within the field of soft robotics. Nevertheless, soft, touchless sensors offer the advantage of non-invasive sensing and gripping without the drawbacks linked to physical contact. Consequently, the popularity of soft touchless sensors has grown in recent years, as they facilitate intuitive and safe interactions with humans, other robots, and the surrounding environment. This review explores the emerging confluence of touchless sensing and soft robotics, outlining a roadmap for deployable soft robots to achieve human-level dexterity.

Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics

Technological advancements have facilitated the development of sophisticated vision systems, integrating optical sensors with artificial vision and machine learning techniques to create applications in different fields of robotics. One such field is Search and Rescue (SAR) robotics, which has historically played a significant role in assisting brigades following post-disaster events, particularly in exploration phases and, crucially, in victim identification. The importance of employing these systems in victim identification lies in their functionality under challenging conditions, enabling the capture of information across different light spectrum ranges (RGB, Thermal, Multispectral). This article proposes an innovative comparative analysis that scrutinizes the advantages and limitations of three sensor types in victim detection. It explores contemporary developments in the state-of-the-art and proposes new metrics addressing critical aspects, such as functionality in specific scenarios and the analysis of environmental disturbances. For the indoor and outdoor testing phase, a quadrupedal robot has been equipped with these cameras. The primary findings highlight the individual contributions of each sensor, particularly emphasizing the efficacy of the infrared spectrum for the thermal camera and the Near Infrared and Red Edge bands for the multispectral camera. Ultimately, following system evaluations, detection precisions exceeding 92% and 86%, respectively, were achieved.

Multivapor-Responsive Controlled Actuation of Starch-Based Soft Actuators.

Multivapor-responsive biocompatible soft actuators have immense potential for applications in soft robotics and medical technology. We report fast, fully reversible, and multivapor-responsive controlled actuation of a pure cassava-starch-based film. Notably, this starch-based actuator sustains its actuated state for over 60 min with a continuous supply of water vapor. The durability of the film and repeatability of the actuation performance have been established upon subjecting the film to more than 1400 actuation cycles in the presence of water vapor. The starch-based actuators exhibit intriguing antagonistic actuation characteristics when exposed to different solvent vapors. In particular, they bend upward in response to water vapor and downward when exposed to ethanol vapor. This fascinating behavior opens up new possibilities for controlling the magnitude and direction of actuation by manipulating the ratio of water to ethanol in the binary solution. Additionally, the control of the bending axis of the starch-based actuator, when exposed to water vapor, is achieved by imprinting-orientated patterns on the surface of the starch film. The effect of microstructure, postsynthesis annealing, and pH of the starch solution on the actuation performance of the starch film is studied in detail. Our starch-based actuator can lift 10 times its own weight upon exposure to ethanol vapor. It can generate force ∼4.2 mN upon exposure to water vapor. To illustrate the vast potential of our cassava-starch-based actuators, we have showcased various proof-of-concept applications, ranging from biomimicry to crawling robots, locomotion near perspiring human skin, bidirectional electric switches, ventilation in the presence of toxic vapors, and smart lifting systems. These applications significantly broaden the practical uses of these starch-based actuators in the field of soft robotics.

What weeding robots need to know about ecology

In weed control the aims of securing crop productivity and protecting biodiversity are often difficult to reconcile. Currently, the development of autonomous in-field intervention technology, such as field robots, is creating new potential for minimizing trade-offs between these two aims. To exploit this potential, weed management strategies need to adapt. However, it is currently unclear which kind of input information (e.g. weed cover, number of weeds, weed species identity) is required for such a targeted approach, and which impacts the robotic application has on the trade-off between crop yield and biodiversity. Here, we used a dataset from organically farmed fields to assess several weed management strategies, simulating robot-supported weed control. Specifically, we used within-field heterogeneity of several weed and crop productivity variables to model effects of different kinds of input information for a hypothetical, spatially selective robotic weed control system. The results showed that, at a defined yield loss, gamma diversity (number of weed species on the entire investigated area) is maintainable to a large degree, even without information on weed or crop heterogeneity within the field being used to decide where to weed. However, to maintain alpha diversity (average number of weed species per plot), more spatially explicit input information is required, such as on the number of species per plot, weed quantity (weed cover per species), and weed competitiveness. Consequently, a weeding robot would have to be technically capable of distinguishing between individual weed species, measuring weed cover, processing captured information in real time and removing weeds at per-plant level. Further, it could be shown that the success of such a complex weed management strategy is independent of the degree of spatial heterogeneity of crop yield and of the present level of weed species richness.

Modelling of Modular Soft Robots: From a Single to Multiple Building Blocks

Despite the advances in soft robots, their modelling is still one of the research challenges due to the complexity and non-linearity of their soft nature. A novel design of reconfigurable soft robots was recently introduced to bring more maturity to the field of soft robots. Here, a modelling study of the building blocks and the assembled soft robot is developed for a deeper understanding of the system and for predicting their behaviours. The model is validated using several sets of individual and multiple building blocks and the results show good tracking between the model and the experiments with reasonable errors. Towards adopting the model in robotic applications, a grasping setup of 2 assembled soft fingers is demonstrated where the model is used to predict the grasping force and compared to feedback sensing. The force prediction reveals the adaptability of the model and its robustness.