Manipulation Tasks Research Articles

A Dual-Arm Robot Cooperation Framework Based on a Nonlinear Model Predictive Cooperative Control

Dual-arm robots show better task adaptability but face more constraints than single-arm robots. Inspired by human cooperation options, a nonlinear model predictive cooperative control (NMPCC) coupled with a cooperative index is proposed in this article. By adjusting the cooperative index, dual-arm cooperation can be classified into four modes: tracking, motion synchronization, impedance, and force priority. Thus, the cooperation operation problem is converted to a multiobjective optimization problem. Then, a nonlinear model predictive solver coupled with the ACADO toolkit is designed to solve the multiobjective optimization problem, where the robotic torque control input can be calculated in real time (less than 1 ms). The motion synchronization, cooperative transportation, and human dual-arm robot interaction experiments were conducted on dual Franka panda robots. Experiments reveal that the NMPCC control coupled with the cooperative index is easy to apply in dual-arm robots and can adapt to different complex manipulation tasks.

A Novel Incipient Slip Degree Evaluation Method and Its Application in Adaptive Control of Grasping Force

Grasping is the most basic and important function of robots. However, the grasping performance of existing manipulators is far inferior to that of human hands. Human hands are able to evaluate the degree of incipient slip on contact surface based on tactile information during grasping process and thus regulate grasping force. As a result, humans can use the smallest possible grasping force while ensuring successful grasping, which maximizes the performance of their hands and avoids damaging the grasped object as much as possible. The existing incipient slip degree evaluation methods have certain shortcomings. So this paper proposes a novel method which can be applied to complex contact conditions where the incipient slip degree is not unidirectional and torque exists. Also, there are no restrictions on the material parameters and surface topography of the grasped object, and no need to obtain any information about it in advance. We construct a grasping force control strategy for parallel grippers based on this evaluation method, with the goal of enabling the gripper to achieve the similar grasping performance of human hands. The grasping strategy is verified in simulations and actual experiments, and the difference between the controlled force and minimum grasping force is demonstrated. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In unstructured grasping tasks, the information of the grasped object cannot be obtained in advance, which makes it difficult for robotic grasping. Conventional grasping strategies often use the maximum force of grippers, but this can cause damage to fragile objects. In contrast, human hands can flexibly control the size of the grasping force, which will not far exceed the minimum force required to ensure successful grasping. This capability greatly expands the grasping range of human hands. Neuroscience studies have shown that such excellent grasping performance owes to the perception of incipient slip by human’s tactile system. However, existing incipient slip detection methods for manipulators have certain drawbacks. To fill this gap, we propose a novel method that can be applied to contact conditions where tangential forces are reversed and torques exist. Meanwhile the method has no restrictions on the surface morphology and material parameters of the contact object, which has a good range of applications. This paper applies this method to a parallel gripper commonly used in industrial scenarios, and proposes a grasping force control strategy that can automatically adjust the magnitude of the grasping force to achieve a similar grasping performance to human hands -keeping the grasping force within a certain range beyond the minimum value. In the future, we also intend to apply the proposed method to more dexterous manipulators and perform more complex manipulation tasks. Possible application scenarios include: smart prosthesis, virtual reality, remote control, etc. Currently, we assume that the Coulomb’s law of friction is satisfied on the contact surface, and more complex friction models will be introduced in the future to further improve the applicability of this method.

Smart Perception for Situation Awareness in Robotic Manipulation Tasks

Smart Perception for Situation Awareness in Robotic Manipulation Tasks

Philosophy Through Computation

We explore the possibility of teaching philosophy through the teaching of computer programming. It is pedagogically useful to use programming because it is extremely popular (especially due to the recent breakthroughs in machine learning), and it can provide a novel, interesting, and clear introduction to a variety of classic philosophical issues. We discuss two examples. The first is using programming to solve digital image manipulation tasks as a way of introducing and clarifying debates over external world skepticism. The second is programming a simulation of a simple 2-D cellular automaton as a way of introducing and clarifying debates over determinism and free will.

Progressively Learning to Reach Remote Goals by Continuously Updating Boundary Goals.

Training an effective policy on complex goal-reaching tasks with sparse rewards is an open challenge. It is more difficult for the task of reaching remote goals (RRG), as the unavailability of the original rewards and large Wasserstein distance between the distributions of desired goals and initial states make existing methods for common goal-reaching tasks inefficient or even completely ineffective. In this article, we propose progressively learning to reach remote goals by continuously updating boundary goals (PLUB), which solves RRG tasks by reducing the Wasserstein distance between the distributions of boundary goals and desired goals. Specifically, the concept of boundary goal is introduced, which is the set of the closest achieved goals for each desired goal. In addition, to reduce the computational complexity caused by the Wasserstein distance, the closest moving distance is introduced, which is its upper bound, and also the expectation of the distance between the desired goal and the closest boundary goal. By selecting the appropriate intermediate goal from all boundary goals and continuously updating boundary goals, both the closest moving distance and the Wasserstein distance can be reduced. As a result, RRG tasks degenerate into common goal-reaching tasks that can be efficiently solved by a combination of hindsight relabeling and the learning from demonstrations (LfD) method. Extensive experiments on several robotic manipulation tasks demonstrate that PLUB can bring substantial improvements over the existing methods.

Curriculum Learning for Robot Manipulation Tasks With Sparse Reward Through Environment Shifts

Curriculum Learning for Robot Manipulation Tasks With Sparse Reward Through Environment Shifts

Whole Body Control of an Autonomous Mobile Manipulator Using Series Elastic Actuators

The flexible joint robot has superior attractive features because of its high mobility, high load ratiohigh torque fidelity, robustness for external disturbance, task adaptability, and safety. In this paper, an autonomous mobile manipulator driven by the designed series elastic actuators (SEAs) is developed. A complete dynamic model of nonholonomic mobile manipulator including a mobile platform and manipulator with joint flexibility operating simultaneously is proposed. An integrated whole body trajectory control framework is proposed for such robot to perform mobile manipulation tasks. Considering the nonholonomic and holonomic constraints in the mobile manipulation, the whole-body dynamics is formulated and reduced. To address the highly nonlinear of the dynamics and model uncertainty, a novel integral Lyapunov function (ILF)-based adaptive neural network control for task tracking under uncertainties of the flexible joint robot model is proposed. Compared with existing methods, the proposed method provides an alternative for controlling flexible joint robots. The feasibility of the proposed method is verified by the extensive trajectory tracking experiment results in our developed flexible joint manipulator.

A Sensory Soft Robotic Gripper Capable of Learning-Based Object Recognition and Force-Controlled Grasping

Soft robotic grippers possess high structural compliance and adaptability, allowing them to grasp objects with unknown and irregular shapes and sizes. To enable more dexterous manipulation, soft sensors that are similar in mechanical properties to common elastomer materials are desired to be integrated into soft grippers. In this paper, we develop ionic hydrogel-based strain and tactile sensors and integrate these sensors into a three-finger soft gripper for learning-based object recognition and force-controlled grasping. Such hydrogel-based sensors have excellent conductivity, high stretchability and toughness, good ambient stability, and unique antifreezing property; they can be readily attached to a soft gripper at desired locations for strain and tactile sensing. By using a deep-learning model, the sensory soft gripper is demonstrated to be capable of grasping and recognizing objects at both room and freezing temperatures, and achieving close to 100% recognition accuracy for ten typical objects. Moreover, the capacitive tactile feedback of the gripper is utilized to develop a closed-loop force controller and realize force-controlled grasping of fragile or highly deformable objects. A new slip detection and compensation strategy is also proposed and validated for the sensory gripper for adjusting the grasping force in real time upon detecting slippage. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The multimodal sensation of a soft robotic gripper could enrich its grasping functionalities and improve its manipulation performance. This research integrates novel antifreezing ionic hydrogel-based strain and tactile sensors into a three-finger soft robotic gripper for learning-based object recognition and force-controlled grasping. Constructed from a highly stretchable, ambient-stable, and antifreezing ionic hydrogel, the strain and tactile sensors can be readily integrated at the desired locations on the soft gripper, and can reliably operate at both ambient and freezing temperatures with excellent mechanical and electrical properties. Based on the feedback of the strain and tactile sensors, a deep learning model is employed to enable high-accuracy object recognition while grasping, which can be useful for manipulation in vision-free environments. Closed-loop force control and slip compensation strategies are also demonstrated for reliably grasping fragile/deformable objects and handling slip events during the manipulation of heavy objects. The sensory soft gripper and the associated object recognition and force control methods could find practical applications in a variety of robotic manipulation tasks.

КЕНЖЕ МЕКТЕП ОКУУЧУЛАРЫНЫН КОМПЕТЕНТТҮҮЛҮГҮН КАЛЫПТАНДЫРУУДА БАЛДАРДЫН ОЙ ЖҮГҮРТҮҮСҮН ӨНҮКТҮРҮҮ

In this article we will talk about mathematical and mathematical-functional financial literacy of younger schoolchildren. The development of memory and thinking of junior schoolchildren, the ability to apply the acquired knowledge in practice is considered as the basis for the formation of functional literacy, mathematical skills. The mathematics lesson discusses the development of logical thinking, verbal counting, conclusions and the depth of Webb's knowledge, problem setting, analysis, which is important for the formation of functional, mathematical-functional, financial literacy among students. In the math textbook for grades 1-4, the availability of financial literacy tasks is analyzed and it is proposed to reformulate them. Several examples and tasks for use in the classroom are analyzed, taking into account the system-activity approach to the development of competencies of younger schoolchildren.

Fast Convergent Ali Baba and Forty Thieves Algorithm for Inverse Kinematic Solution of 7-DOF Robotic Manipulator

Inverse kinematics is a crucial topic in robotics, enabling robots to calculate the joint angles required to achieve the desired end effector position and orientation. Solving the inverse kinematics problem quickly with high accuracy is vital for robot manipulators. If sufficient speed is provided, the real-time motion planning task of robot manipulators can be achieved. Real-time motion planning of complex robot manipulators is not possible with classical mathematical methods. Overcoming this problem will provide many benefits in the design and control of robot manipulators. In contemporary research, metaheuristic approaches have become widely employed for addressing the inverse kinematics problem. This investigation utilizes the efficient and simple Ali Baba and the Forty Thieves (AFT) algorithm to resolve the inverse kinematics problem. To increase convergence speed of AFT, a parameter has been used in early iterations of the algorithm to prevent thieves from randomly searching within the search area to find Ali Baba when they realized they had been deceived. Additionally, an approach has been proposed regarding the accuracy of the information brought by the Marjaneh. Finally, the inverse kinematics solution of the 7-DOF robot manipulator was carried out comparatively.

Stable posture tracking for modular spatial hyper-redundant serial arms using selective control points

ABSTRACT Robotic arms with redundant degrees of freedom are known to exhibit characteristics that make them more suitable than their non-redundant counterparts in many manipulation tasks. These redundant degrees of freedom allow for multiple control objectives to be pursued simultaneously leading to increased efficiency, dexterity and versatility. Redundancy however brings about structural complexity, difficulties in tasks specification, challenging kinematics and dynamics and high computational cost. This work tackles kinematic hyper redundancy resolution in the context of posture (shape) control of a modular serial link arm by proposing a new take on formulating and solving the problem. In the proposed formulations, strategic points along the arm (termed control points) are explicitly controlled in such way that their overall motion steers the posture of the arm toward the desired target posture. These control points stem from the modular structure of the arm and are chosen in order to achieve specific control characteristics. Desired target posture outlines are specified intuitively and arbitrarily with continuous spline curves. The proposed method has demonstrated the ability to safely steer the arm towards intermediate postures which reduce the overall posture tracking error. It copes well with singularities and is capable of tracking target postures that are significantly distinct from the arm's initial posture. Numerical simulations on a 40 DOF arm are conducted to assess the validity and performance of the method.

Deep CNN-Based Static Modeling of Soft Robots Utilizing Absolute Nodal Coordinate Formulation.

Soft continuum robots, inspired by the adaptability and agility of natural soft-bodied organisms like octopuses and elephant trunks, present a frontier in robotics research. However, exploiting their full potential necessitates precise modeling and control for specific motion and manipulation tasks. This study introduces an innovative approach using Deep Convolutional Neural Networks (CNN) for the inverse quasi-static modeling of these robots within the Absolute Nodal Coordinate Formulation (ANCF) framework. The ANCF effectively represents the complex non-linear behavior of soft continuum robots, while the CNN-based models are optimized for computational efficiency and precision. This combination is crucial for addressing the complex inverse statics problems associated with ANCF-modeled robots. Extensive numerical experiments were conducted to assess the performance of these Deep CNN-based models, demonstrating their suitability for real-time simulation and control in statics modeling. Additionally, this study includes a detailed cross-validation experiment to identify the most effective model architecture, taking into account factors such as the number of layers, activation functions, and unit configurations. The results highlight the significant benefits of integrating Deep CNN with ANCF models, paving the way for advanced statics modeling in soft continuum robotics.

Огляд досліджень в напрямку міоелектричного методу керування біонічним протезом

Myoelectric control of bionic prostheses is an important field of research in the field of rehabilitation. Intuitive and intelligent myoelectric control can restore upper limb function. However, much research now focuses on the development of various myoelectrical and biotechnical control methods, limiting research to the complex daily tasks of prosthetic manipulation, such as grasping and releasing. The article examines the latest advances in the research areas of bionic prosthesis management. In particular, attention is paid to the methods of determining movement intentions, classification of discrete movements, estimation of continuous movements, single-channel control, feedback control and combined control. Motor neurons group input signals from the central nervous system that affect muscles and form motor units. The electromyography (EMG) signal, which is obtained by recording motor neuron action potentials, reflects muscle activity. This signal, oscillating within ±5000 μV with a frequency of 6 to 500 Hz, reflects the characteristics of muscle contraction. Depending on the location of the sensors, EMG signals are divided into intramuscular and surface electromyography. Intramuscular electromyography provides an accurate study of muscle activation, but requires the implantation of sensors, which can lead to physical problems. EMG, which captures a signal from the surface of the skin, is easier to use and is widely used in experiments with myoelectric prostheses.

Design optimizer for planar soft-growing robot manipulators

Soft-growing robots are innovative devices that feature plant-inspired growth to navigate environments. Thanks to their embodied intelligence of adapting to their surroundings and the latest innovations in actuation and manufacturing, it is possible to employ them for specific manipulation tasks. The applications of these devices include exploration of delicate/dangerous environments, manipulation of items, or assistance in domestic environments.This work presents a novel approach for design optimization of soft-growing robots, which will be used prior to manufacturing to suggest to engineers – or robot designer enthusiasts – the optimal size of the robot to be built for solving a specific task. The design process is modeled as a multi-objective optimization problem, optimizing the kinematic chain of a soft manipulator to reach targets and avoid unnecessary overuse of material and resources. The method exploits the advantages of population-based optimization algorithms, in particular evolutionary algorithms, to transform the problem from multi-objective into single-objective thanks to an efficient mathematical formulation, the novel rank-partitioning algorithm, and obstacle avoidance integrated within the optimizer operators. The proposed method was tested on different tasks to assess its optimality, which showed significant performance in solving the problem: the retrieved designs are short, smooth, and precise at reaching targets. Finally, comparative experiments show that the proposed method works better than the one existing in the literature in terms of precision (14% higher), resource consumption (2% shorter configurations with 4% fewer links), actuation (85% less wavy and undulated configurations), and run time (13% faster).

Bio-inspired affordance learning for 6-DoF robotic grasping: A transformer-based global feature encoding approach

The 6-Degree-of-Freedom (6-DoF) robotic grasping is a fundamental task in robot manipulation, aimed at detecting graspable points and corresponding parameters in a 3D space, i.e affordance learning, and then a robot executes grasp actions with the detected affordances. Existing research works on affordance learning predominantly focus on learning local features directly for each grid in a voxel scene or each point in a point cloud scene, subsequently filtering the most promising candidate for execution. Contrarily, cognitive models of grasping highlight the significance of global descriptors, such as size, shape, and orientation, in grasping. These global descriptors indicate a grasp path closely tied to actions. Inspired by this, we propose a novel bio-inspired neural network that explicitly incorporates global feature encoding. In particular, our method utilizes a Truncated Signed Distance Function (TSDF) as input, and employs the recently proposed Transformer model to encode the global features of a scene directly. With the effective global representation, we then use deconvolution modules to decode multiple local features to generate graspable candidates. In addition, to integrate global and local features, we propose using a skip-connection module to merge lower-layer global features with higher-layer local features. Our approach, when tested on a recently proposed pile and packed grasping dataset for a decluttering task, surpassed state-of-the-art local feature learning methods by approximately 5% in terms of success and declutter rates. We also evaluated its running time and generalization ability, further demonstrating its superiority. We deployed our model on a Franka Panda robot arm, with real-world results aligning well with simulation data. This underscores our approach’s effectiveness for generalization and real-world applications.

Partial Consistency for Stabilizing Undiscounted Reinforcement Learning.

Undiscounted return is an important setup in reinforcement learning (RL) and characterizes many real-world problems. However, optimizing an undiscounted return often causes training instability. The causes of this instability problem have not been analyzed in-depth by existing studies. In this article, this problem is analyzed from the perspective of value estimation. The analysis result indicates that the instability originates from transient traps that are caused by inconsistently selected actions. However, selecting one consistent action in the same state limits exploration. For balancing exploration effectiveness and training stability, a novel sampling method called last-visit sampling (LVS) is proposed to ensure that a part of actions is selected consistently in the same state. The LVS method decomposes the state-action value into two parts, i.e., the last-visit (LV) value and the revisit value. The decomposition ensures that the LV value is determined by consistently selected actions. We prove that the LVS method can eliminate transient traps while preserving optimality. Also, we empirically show that the method can stabilize the training processes of five typical tasks, including vision-based navigation and manipulation tasks.

The dynamics of auditory working memory impairment in logopenic aphasia and Alzheimer’s disease

AbstractBackgroundImpaired auditory verbal working memory is a diagnostic hallmark and integral driver of the clinical phenotype in logopenic aphasia. However, the physiology of the working memory buffer in this syndrome is poorly characterised. Here we addressed the temporal dynamics of auditory verbal working memory in patients with logopenic aphasia and typical Alzheimer’s disease.MethodIn a cohort of patients with logopenic aphasia and typical Alzheimer’s disease and in healthy age‐matched controls, we assessed how temporal manipulations of standard auditory verbal working memory tasks (forward and reverse digit span and phrasal repetition) affected performance. We varied tempo of delivery and inter‐trial gap and performed a detailed analysis of error types. Participants also had pure tone audiometry to assess peripheral hearing function and a comprehensive general neuropsychological assessment.ResultCompared with healthy controls, patients with logopenic aphasia and Alzheimer’s disease showed increased sensitivity to temporal manipulation of auditory verbal working memory tasks and error profiles suggesting a dynamic physiological lesion of the working memory buffer. These effects were particularly marked in the logopenic group.ConclusionOur findings open a novel physiological window on working memory dynamics in Alzheimer’s disease syndromes. Further work is warranted to assess how this dynamic deficit impacts communication in patients’ daily lives, how it can best inform management interventions and its potential as a novel, rapid read‐out of neural function in the era of disease‐modifying therapies.

Robust planning for multi-stage forceful manipulation

Multi-step forceful manipulation tasks, such as opening a push-and-twist childproof bottle, require a robot to make various planning choices that are substantially impacted by the requirement to exert force during the task. The robot must reason over discrete and continuous choices relating to the sequence of actions, such as whether to pick up an object, and the parameters of each of those actions, such how to grasp the object. To enable planning and executing forceful manipulation, we augment an existing task and motion planner with constraints that explicitly consider torque and frictional limits, captured through the proposed forceful kinematic chain constraint. In three domains, opening a childproof bottle, twisting a nut and cutting a vegetable, we demonstrate how the system selects from among a combinatorial set of strategies. We also show how cost-sensitive planning can be used to find strategies and parameters that are robust to uncertainty in the physical parameters.

Effects of free versus restricted arm movements on postural control in normal and modified sensory conditions in young and older adults

The purpose of this study was to explore the effects of arm movements on postural control when standing under different sensory conditions in healthy young and older adults. Fifteen young (mean ± SD age; 21.3 ± 4.2 years) and 15 older (mean ± SD age; 73.3 ± 5.0 years) adults completed the modified Romberg test, which uses four task manipulations (i.e. eyes open and eyes closed on a firm and foam surface) to compromise the fidelity of sensory feedback mechanisms. Each participant completed the tasks under two arm movement conditions: restricted and free arm movements. Centre of pressure (COP) range and frequency were calculated to characterise postural performance and strategy, respectively. Older adults showed greater COP range with restricted compared to free arm movements during all modified sensory conditions, with these effects most prominent in the medio-lateral (ML) plane (all p < .05, Cohen's d = 0.69–1.61). Compared to the free arm movement condition, there was an increase in ML displacement and frequency when arm movements were restricted during only the most challenging (i.e. vestibular dominant) task in young adults (all p < .05, d = 0.645–0.83). Finally, main age effects for the arm restriction cost (p < .05) indicates a greater reliance on an upper body strategy in older compared to young adults, independent of sensory availability/accuracy. These findings indicate that older adults compensate for the loss of accuracy in sensory input by increasing reliance on upper body movement strategies.

2D of Robotic Arm Based on Inverse Kinematics Using Arduino

Abstract: The advancements and quick developments in the technology has made it possible to create a platform for the invention of new as well as great things. One of the concept that is being used lately to improve the technology is the concept of robotics. This project focuses on the implementation of inverse kinematics for a 2-DOF (two degrees of freedom) robot arm using an Arduino microcontroller. The inverse kinematics equations are derived and programmed to calculate the joint angles required to position the robot arm's end effector in a desired location within its workspace. The Arduino board is used to interface with the robot arm's actuators and control the movement of the arm. The Arduino programming aspect of the project involves developing a control algorithm that accepts target coordinates for the end effector and computes the necessary joint angles using inverse kinematics equations. Servo motors are manipulated through the Arduino Servo library to achieve the calculated angles, enabling the arm to reach the desired positions accurately and efficiently. To enhance the functionality and safety of the 2-DOF robotic arm, the project includes features such as calibration routines, testing procedures, and safety measures to avoid collisions and servo load. The project aims to demonstrate the application of inverse kinematics in controlling robotic manipulators for precise positioning tasks