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This paper aims to propose an innovative kinematic control algorithm for redundant robotic manipulators. The algorithm takes advantage of a bio-inspired approach.

A simplified two-degree-of-freedom model is presented to handle kinematic redundancy in the x-y plane; an extension to three-dimensional tracking tasks is presented as well. A set of sample trajectories was used to evaluate the performances of the proposed algorithm.

The results from the simulations confirm the continuity and accuracy of generated joint profiles for given end-effector trajectories as well as algorithm robustness, singularity and self-collision avoidance.

This paper shows how to control a redundant robotic arm by applying human upper arm-inspired concept of inter-joint dependency.

This paper aims to describe the design of a multi-degree of freedom (DOF) prosthetic hand prototype implementing postural synergy mechanically, which is actuated by two motors via a transmission unit, and is controlled using surface electromyography (sEMG) signal.

First, an anthropomorphic robotic hand is designed to imitate the human hand. The robotic hand has 18 DOF, 12 of which are actively driven by Bowden cables. Next, a set of different grasp modes are performed on a “full actuation” robotic hand, and principal component analysis (PCA) method is used to extract the first two postural synergies. Then, they are used to design a differential pulley-based transmission unit using two independent inputs to drive 12 output tendons. Finally, two control signals extracted from six channels of sEMG signals are used to proportionally control the two motors for achieving hand posture synthesis.

Using a differential pulley-based mechanical transmission unit to implement the synthesis of the first two postural synergies can make the prosthetic hand achieve different grasps by two motors, such as power, precision and lateral grasps. It is also feasible to control this “two actuation” prosthetic hand by relating the two-dimensional sEMG inputs with the first two postural synergies.

Mechanical implantation of postural synergies reduces the number of independent actuators without sacrificing the prosthetic hand’s versatility and simplifies its controller. Two-dimensional control extracted from sEMG is mapped into the combination coefficients of postural synergy synthesis. It shows potential application in the practical prosthetic hand.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

The purpose of this paper is to model and predict suitable gait trajectories of lower-limb exoskeleton for wearer during rehabilitation walking. Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict suitable gait trajectories for wearer.

In this paper, the authors propose a Deep Spatial-Temporal Model (DSTM) for generating knee joint trajectory of lower-limb exoskeleton, which first leverages Long-Short Term Memory framework to learn the inherent spatial-temporal correlations of gait features.

With DSTM, the pathological knee joint trajectories can be predicted based on subject’s other joints. The energy expenditure is adopted for verifying the effectiveness of new recovery gait pattern by monitoring dynamic heart rate. The experimental results demonstrate that the subjects have less energy expenditure in new recovery gait pattern than in others’ normal gait patterns, which also means the new recovery gait is more suitable for subject.

Long-Short Term Memory framework is first used for modeling rehabilitation gait, and the deep spatial–temporal relationships between joints of gait data can obtained successfully.

Lower‐limb exoskeletons and powered orthoses are external devices that assist patients with locomotive disorders to achieve correct limb movements. Current batteries cannot meet the long‐term power requirements for these devices, which operate for long periods of time. This issue has become a major challenge in the development of these portable robots. Conversely, legged locomotion in animals and humans is efficient; to emulate this behaviour, biomimetic actuation has been designed attempting to incorporate elements that resemble biological elements, such as tendons and muscles, in the mechanical systems. The purpose of this paper is to present a mechanism that resembles a human tendon to achieve and utilise the synergic actuation of the leg joints.

In this paper, we present a mechanism that resembles a human tendon to move the ankle joint and utilise the synergic actuation of hip and knee joints. Implementation of the proposed transmission system in the ATLAS active orthosis prototype allowed for a better ankle gait fit, which resulted in a more natural stride and, as expected, optimised energy consumption in the locomotion cycle and actuation energy requirements.

The fitted passive ankle motion provides toe‐off impulse, increases support force, and helps provide ground clearance.

A synergetic underactuated movement in the ankle joint, implemented by two cables in each leg, improves the functionality of the device without increasing the leg weight and while maintaining a reduced size. To achieve a correct and efficient motion in the ankle of an active orthosis, two steel cables were attached in the ATLAS orthosis. These cables act as a synergic biarticular linkage and transfer motion from the hip and knee joints. Synergic ankle motion provides impulse in the toe‐off, increases support force, and provides ground clearance. These goals are achieved with low energy expenditure because of synergical actuation, and high inertia is prevented in the more distal limb.

The purpose of this paper is to design an exoskeleton robot and present a corresponding rehabilitation training method for patients in different rehabilitation stages.

This paper presents a lightweight seven-degrees-of-freedom (DOF) cable-driven exoskeleton robot that is wearable and adjustable. After decoupling joint movement caused by a cable-driven mechanism, active rehabilitation training mode and passive rehabilitation training mode are proposed to improve the effect of rehabilitation training.

Simulations and experiments have been carried out, and the results validated the feasibility of the proposed mechanism and methods by a fine rehabilitative effect with different persons.

This paper designed a 7-DOF cable-driven exoskeleton robot that is suitable for patients of different body measurements and proposed the active rehabilitation training mode and passive rehabilitation training mode based on the cable-driven exoskeleton robot.

This paper aims to optimize the stiffness coefficient of the elastic element for a passive waist assistive exoskeleton (WAE). There is a tradeoff between stiffness coefficient of elastic element of the exoskeleton and work efficiency of the wearer, because elastic element affects original bending motion of the wearer and the force requirement of erector spinae is compensated by the other muscles. However, there is no accepted conclusion on how to determine the proper stiffness coefficient, especially with respected to the effort of groups of muscles, not only erector spinae.

In this study, a consumption indicator based on muscle fatigue of seven muscles is proposed and a Bayesian-based human-in-the-loop optimization approach is adopted to optimize the stiffness coefficient. Pneumatic artificial muscles are used to replace the mechanical elastic part to adjust the assistive force automatically. The proposed optimization method is verified by the way of load-lifting experiments with three different conditions: without exoskeleton, with fixed air pressure and with optimized air pressure. Six subjects participated in the experiment and each experiment is performed in different day.

Compared with No-Exo condition and static assistance condition, the parameter-optimized waist exoskeleton averagely reduces muscle fatigue of the six subjects by 45.30 ± 29.14% and 30.94 ± 30.29%, respectively. The experimental results indicate that the proposed method is effective to reduce muscle fatigue during stoop lifting task.

This paper provides a novel cost function construction method based on muscle fatigue and muscle synergy for passive WAE stiffness optimization.

This paper presents a method to elaborate the selections of these parameters to achieve stable grasps. The performance of a prosthetic hand is mainly determined by its mechanical design. However, the effects of the geometric parameters of the hand configuration and the object sizes on the grasp stability are unknown.

First, the thumb functions of human hands are analyzed based on the anatomical model, and the configuration characteristics of the thumbs for typical prosthetic hands are summarized. Then a method of optimizing the thumb configuration is proposed by measuring the kinematic transmission performance of robotics. On the basis of the thumb configuration analysis, a design method of the prosthetic hand configuration is proposed based on form closure theory. The discriminant function of form closure is used to analyze and determine the hand configuration parameters.

An application of this method – the newly developed HIT V prosthetic hand – elaborates the optimization of the thumb configuration and the hand configuration, where the relation between the key hand configuration parameters and the discriminant function on condition of satisfying form closure, sustained by analytical equations and graphs, is revealed and visualized. An experimental verification shows that it is an effective method to design the prosthetic hand configuration available for grasping typical objects in our daily life.

The paper shows how to easily determine the geometric dimensions of the palm, phalanges and hand configuration, so that the desired range of object sizes can be obtained.

Rigid robotic hands are generally fast, precise and capable of exerting large forces, whereas soft robotic hands are compliant, safe and adaptive to complex environments. It is valuable and challenging to develop soft-rigid robotic hands that have both types of capabilities. The paper aims to address the challenge through developing a paradigm to achieve the behaviors of soft and rigid robotic hands adaptively.

The design principle of a two-joint finger is proposed. A kinematic model and a stiffness enhancement method are proposed and discussed. The manufacturing process for the soft-rigid finger is presented. Experiments are carried out to validate the accuracy of the kinematic model and evaluate the performance of the flexible body of the finger. Finally, a robotic hand composed of two soft-rigid fingers is fabricated to demonstrate its grasping capacities.

The kinematic model can capture the desired distal deflection and comprehensive shape accurately. The stiffness enhancement method guarantees stable grasp of the robotic hand, without sacrificing its flexibility and adaptability. The robotic hand is lightweight and practical. It can exhibit different grasping capacities.

It can be applied in the field of industrial grasping, where the objects are varied in materials and geometry. The hand’s inherent characteristic removes the need to detect and react to slight variations in surface geometry and makes the control strategies simple.

This work proposes a novel robotic hand. It possesses three distinct characteristics, i.e. high compliance, exhibiting discrete or continuous kinematics adaptively, lightweight and practical structures.

Pediatric disorders, such as cerebral palsy and stroke, can result in thumb-in-palm deformity greatly limiting hand function. This not only limits children's ability to perform activities of daily living but also limits important motor skill development. Specifically, the isolated orthosis for thumb actuation (IOTA) is 2 degrees of freedom (DOF) thumb exoskeleton that can actuate the carpometacarpal (CMC) and metacarpophalangeal (MCP) joints through ranges of motion required for activities of daily living. The paper aims to discuss these issues.

IOTA consists of a lightweight hand-mounted mechanism that can be secured and aligned to individual wearers. The mechanism is actuated via flexible cables that connect to a portable control box. Embedded encoders and bend sensors monitor the 2 DOF of the thumb and flexion/extension of the wrist. A linear force characterization was performed to test the mechanical efficiency of the cable-drive transmission and the output torque at the exoskeletal CMC and MCP joints was measured.

Using this platform, a number of control modes can be implemented that will enable the device to be controlled by a patient to assist with opposition grasp and fine motor control. Linear force and torque studies showed a maximum efficiency of 44 percent, resulting in a torque of 2.39±1.06 in.-lbf and 0.69±0.31 in.-lbf at the CMC and MCP joints, respectively.

The authors envision this at-home device augmenting the current in-clinic and at-home therapy, enabling telerehabilitation protocols.

This paper presents the design and characterization of a novel device specifically designed for pediatric grasp telerehabilitation to facilitate improved functionality and somatosensory learning.