A straightforward and miniature implementation method of postural synergies for replicating human grasp characteristics accurately and intuitively

The postural synergies have great potentials to replicate human grasp characteristics, simplify grasp control and reduce the number of hardware needed actuators. However, due to the complex mapping relationship and jagged transmission ratio, the implemented mechanisms are always too bulky and loose which greatly limits its application. For current solutions, the replicating accuracy of motion characteristics or control intuition are compromised, and hitherto no work reports the replicating errors in literatures. To overcome these limitations, we present a novel design framework to determine the actuation configuration, implemented scheme and physical parameters. In this way, the mechanism is miniaturized and can be compactly embedded in hand palm, a self-contained synergy robot hand that integrated with mechanism, sensors and suited electrical system is built. The experiments demonstrate that the robot hand can accurately replicate the motion characteristics of two primary synergies, keep the control intuition to simplify grasp control, perform a better anthropomorphic motion capability and grasp different objects with versatile grasp functionality.

Robot hand-eye cooperation based on improved inverse reinforcement learning

Purpose How to make accurate action decisions based on visual information is one of the important research directions of industrial robots. The purpose of this paper is to design a highly optimized hand-eye coordination model of the robot to improve the robots’ on-site decision-making ability. Design/methodology/approach The combination of inverse reinforcement learning (IRL) algorithm and generative adversarial network can effectively reduce the dependence on expert samples and robots can obtain the decision-making performance that the degree of optimization is not lower than or even higher than that of expert samples. Findings The performance of the proposed model is verified in the simulation environment and real scene. By monitoring the reward distribution of the reward function and the trajectory of the robot, the proposed model is compared with other existing methods. The experimental results show that the proposed model has better decision-making performance in the case of less expert data. Originality/value A robot hand-eye cooperation model based on improved IRL is proposed and verified. Empirical investigations on real experiments reveal that overall, the proposed approach tends to improve the real efficiency by more than 10% when compared to alternative hand-eye cooperation methods.

Brain Activity Reflects Subjective Response to Delayed Input When Using an Electromyography-Controlled Robot

In various experimental settings, electromyography (EMG) signals have been used to control robots. EMG-based robot control requires intrinsic parameters for control, which makes it difficult for users to understand the input protocol. When a proper input is not provided, the response time of the system varies; as such, the user’s subjective delay should be investigated regardless of the actual delay. In this study, we investigated the influence of the subjective perception of delay on brain activation. Brain recordings were taken while subjects used EMG signals to control a robot hand, which requires a basic processing delay. We used muscle synergy for the grip command of the robot hand. After controlling the robot by grasping their hand, one of four additional delay durations (0 ms, 50 ms, 125 ms, and 250 ms) was applied in every trial, and subjects were instructed to answer whether the delay was natural, additional, or whether they were not sure. We compared brain activity based on responses (“sure” and “not sure”). Our results revealed a significant power difference in the theta band of the parietal lobe, and this time range included the interval in which the subjects could not feel the delay. Our study provides important insights that should be considered when constructing an adaptive system and evaluating its usability.

Development of a Verbal Robot Hand Gesture Recognition System

This article analyzes the most famous sign languages, the correlation of sign languages, and also considers the development of a verbal robot hand gesture recognition system in relation to the Kazakh language. The proposed system contains a touch sensor, in which the contact of the electrical property of the user's skin is measured, which provides more accurate information for simulating and indicating the gestures of the robot hand. Within the framework of the system, the speed and accuracy of recognition of each gesture of the verbal robot are calculated. The average recognition accuracy was over 98%. The detection time was 3ms on a 1.9 GHz Jetson Nano processor, which is enough to create a robot showing natural language gestures. A complete fingerprint of the Kazakh sign language for a verbal robot is also proposed. To improve the quality of gesture recognition, a machine learning method was used. The operability of the developed technique for recognizing gestures by a verbal robot was tested, and on the basis of computational experiments, the effectiveness of algorithms and software for responding to a verbal robot to a voice command was evaluated based on automatic recognition of a multilingual human voice. Thus, we can assume that the authors have proposed an intelligent verbal complex implemented in Python with the CMUSphinx communication module and the PyOpenGL graphical command execution simulator. Robot manipulation module based on 3D modeling from ABB.

Design and Research of Wrist-Assisted Recovery Robot Hand

With the gradual development of the times, people’s material life has been greatly improved. All kinds of sports have become an indispensable part of people’s life, but sports are always accompanied by certain risks. People injured by sports show an increasing trend every year. However, it can be found that among many rehabilitation instruments, there is not much research on wrist rehabilitation instruments, which leads to the restoration of bandage splint or plaster fixation in most of the time. These traditional methods have the problems of slow recovery cycle, difficult disassembly and complex replacement. At the same time, most of the wrist rehabilitation instruments that have been developed are large and medium-sized instruments, which can not be carried, can not play a real-time protective role, and can not be well applied to daily life. Therefore, a wearable periodic recovery robot with traditional strap splint, modern transmission and flexible positioning is proposed.

Systematic object-invariant in-hand manipulation via reconfigurable underactuation: Introducing the RUTH gripper

We introduce a reconfigurable underactuated robot hand able to perform systematic prehensile in-hand manipulations regardless of object size or shape. The hand utilizes a two-degree-of-freedom five-bar linkage as the palm of the gripper, with three three-phalanx underactuated fingers, jointly controlled by a single actuator, connected to the mobile revolute joints of the palm. Three actuators are used in the robot hand system in total, one for controlling the force exerted on objects by the fingers through an underactuated tendon system, and two for changing the configuration of the palm and, thus, the positioning of the fingers. This novel layout allows decoupling grasping and manipulation, facilitating the planning and execution of in-hand manipulation operations. The reconfigurable palm provides the hand with a large grasping versatility, and allows easy computation of a map between task space and joint space for manipulation based on distance-based linkage kinematics. The motion of objects of different sizes and shapes from one pose to another is then straightforward and systematic, provided the objects are kept grasped. This is guaranteed independently and passively by the underactuated fingers using a custom tendon routing method, which allows no tendon length variation when the relative finger base positions change with palm reconfigurations. We analyze the theoretical grasping workspace and grasping and manipulation capability of the hand, present algorithms for computing the manipulation map and in-hand manipulation planning, and evaluate all these experimentally. Numerical and empirical results of several manipulation trajectories with objects of different size and shape clearly demonstrate the viability of the proposed concept.