Re-grasping behavior acquisition by evolutionary programming

The control of multi-fingered robot hands has been the subject of interest. To manipulate some object with regrasping motion, there are many parameters to be determined; the grasping points, grasping forces, regrasping phases, finger allocation and so on. It is difficult to optimize such manipulation parameters for achieving effective manipulation. We propose a generation method of regrasping motion for a four-fingered robot hand using evolutionary programming (EP). In this case, EP determines not only finger motions, but also rotation of the target object in order to increase its manipulability. In manipulating an object like a rectangular solid, it is very important to change the object directions and to grasp another face according to the task progress because the grasping faces tend to be limited to only two. The evolutionary optimization method is generally able to find optimal solutions without a supervisor after much iteration, which makes it almost impractical to apply a real robot directly. Therefore we control the real robot hand with an optimal controller that has been generated in computer simulations. We show the effectiveness of the proposed acquisition method for the regrasping motion with experimental results.

We have proposed a Whole Finger Manipulation (WFM) to improve robustness of manipulation by multifingered robot hand. The WFM is characterized by (1) multiple contact points between an object and surfaces of the inner links of multifingered robot hand, (2) a manipulation by utilizing a sliding at each contact point. In this paper, considering the kinematics and dynamics of the WFM, we propose a control scheme which guarantees the manipulation stability. The proposed control law consists of both tracking control of multifingered robot hand and impedance control which enforces the motion of the contact point in the normal direction and internal force. The minimal internal force is defined as the internal force which generate the minimal contact force at the instant. The conditions for being manipulated an object stably are presented. The experimental results to verify the validity of the proposed control law are shown.

Advanced Human-In-The-Loop (HITL) control strategies for robot hands based on surface electromyography (sEMG) are among major research questions in robotics. Due to intrinsic complexity and inaccuracy of labeling procedures, unsupervised regression of sEMG signals has been employed in literature, however showing several limitations in realizing multiple grasping motion control. In this work, we propose a novel Human-Robot interface (HRi) based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">self-supervised</i> regression of sEMG signals, combining Non-Negative Matrix Factorization (NMF) with Deep Neural Networks (DNN) in order to both avoid explicit labeling procedures and have powerful nonlinear fitting capabilities. Experiments involving 10 healthy subjects were carried out, consisting of an offline session for systematic evaluations and comparisons with traditional unsupervised approaches, and an online session for assessing real-time control of a wearable anthropomorphic robot hand. The offline results demonstrate that the proposed self-supervised regression approach overcame traditional unsupervised methods, even considering different robot hands with dissimilar kinematic structures. Furthermore, the subjects were able to successfully perform online control of multiple grasping motions of a real wearable robot hand, reporting for high reliability over repeated grasp-transportation-release tasks with different objects. Statistical support is provided along with experimental outcomes.

Problem statement: Sensitization of robot hand is still remaining as crucial issue since most of robot hand systems nowadays are only capable to grasp a predefined specific object. It is still difficult for robot hand system to realize human-like tactile sensation. Some common problems in robot hand system are low accuracy sensing device, sensors are not robust enough for long time work and heavy duties, inconsistence tactile sensing detection and difficulties in control of sensing fusion with robot trajectory. These problems are apparently drawback the progress to commercializing robot hands as real consumer products. Approach: This study presented the application of optical three-axis tactile sensor to robot hand to improve sensitization quality in robotic hand system. The proposed tactile sensor system was designed with compliance modules to communicate with robot hand control system. The sensing principle used in this tactile sensor comparatively provides better sensing accuracy to detect contact phenomena from acquisition of three axial directions of forces. Methodology of force and slippage detection in the tactile sensor system was presented. Accordingly, the optimization of robot hand control algorithm to comply with the tactile sensor system was presented and verified in experiment of grasping and twisting. Results: The tactile sensor presented in this study is capable of detecting normal and shear force simultaneously. The proposed methodology was verified in experiment with paper cup and water, in which the result shows the robot control system managed to respond to the proposed object stiffness distinction parameters and effectively respond to sudden change of object weight during grasping. An experiment of grasping and twisting motions was conducted using a bottle cap. In order to perform simultaneous grasping and twisting tasks, optimization of the control algorithm was conducted with additional parameters to satisfy the desired tasks. Conclusion: Experimental result shows that the robot hand managed to perform grasping and twisting of bottle cap smoothly. The overall results revealed good performance of the proposed optical three-axis tactile sensor system and robot hand control algorithm for future application in a real artificial robot hand. In addition, slippage sensation measured in a robot control system could contribute a better maneuvering of the robot arm-finger system.

A unified approach for the control of multifingered robot hands

By considering the dexterous multi-fingered manipulation problem as a hybrid one, we at first propose a MLD model which encapsulates occurrence of impacts, switching between types of contact motion, and phases of continuous motion. Then we deal with planning and tracking problem of the derived MLD system, propose to realize dexterous hand manipulation via MIQP. The effectiveness of the proposed approach is validated by simulation results

Manipulation using dextrous robot hands has been an exciting yet frustrating research topic for the last several years. While significant progress has occurred in the design, construction, and low level control of robotic hands, researchers are up against fundamental problems in developing algorithms for real-time computations in multi-sensory processing and motor control. The aim of this book is to explore parallels in sensorimotor integration in dextrous robot and human hands, addressing the basic question of how the next generation of dextrous hands should evolve. By bringing together experimental psychologists, kinesiologists, computer scientists, electrical engineers, and mechanical engineers, the book covers topics that range from human hand usage in prehension and exploration, to the design and use of robotic sensors and multi-fingered hands, and to control and computational architectures for dextrous hand usage. While the ultimate goal of capturing human hand versatility remains elusive, this book makes an important contribution to the design and control of future dextrous robot hands through a simple underlying message: a topic as complex as dextrous manipulation would best be addressed by collaborative, interdisciplinary research, combining high level and low level views, drawing parallels between human studies and analytic approaches, and integrating sensory data with motor commands. As seen in this text, success has been made through the establishment of such collaborative efforts. The future will hold up to expectations only as researchers become aware of advances in parallel fields and as a common vocabulary emerges from integrated perceptions about manipulation.

Force and touch telepresence has an important effect on robot hand controlling. When controlling humanoid robotic hand has data glove, the movement of robot hand appeared to be rigid because of the lack of touch telepresence. The movement of humanoid robot hand would be more flexible if tactile equipment was installed on data glove. There are two important requirements in tactile equipment of data glove, that is to say the structure of equipment must be compact. Too much complexity would lower the reliability of data glove, on the other hand, and the equipment must simulate the touch telepresence as actual as possible, which would influence the realness of the operation directly. In order to balance the structure of the data glove and the realness of tactile simulation, a new system that could build tactile telepresence between robot hand and human hand has been designed and manufactured, including a data glove and a humanoid robot hand. The data glove has 12 attitude transducers on the back and 14 groups of silica gel electrodes to irritate human hand by electrical pulses and simulate the touch telepresence. The humanoid robot hand use cord to transmit the motion which has 13 servo to control the movement of each joint accurately and 14 accurate pressure sensor to assess the grab situation.

This paper presents the tuning of PID control parameters for position feedback control of motor and the position reference design for grasping task by a 7-degree of freedom (DOF) three-fingered robotic hand. The system of the robotic hand consists of DC-motors, magnetic encoder and a motion controller unit. The robotic hand has three fingers where each finger has two joints and two links made of perspective plastic material. The PID tuning is implemented to all six motors that are used to move all joints of the three fingers. Trial and error method is used for tuning the parameter of PID control to improve the transient response performance of the motors. For the position reference design, a set of position data is identified to produce the grasping trajectory for all finger joints. The trajectory for the robotic hand performs two types of finger configuration for grasping two different shapes of household solid objects. Both works were implemented via programming in Motion Manager Application. It has been observed that the robot hand has performed well with the tuned PID parameters by reducing the overshoot, rise time (T r ), peak time (T p ), settling time (T s ) and steady state error (e ss ). Besides, the robot hand has also succeeded the grasping task with the proposed position reference trajectory.

One of the major challenges in robotics consists in developing successful control strategies for robotic grasping devices. In this scenario, one of the most interesting approaches regards the exploitation of surface electromyography(sEMG). In this work, we propose a novel sEMG-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimally supervised</i> regression approach capable of performing nonlinear fitting without the necessity for point-by-point training data labelling. The proposed method exploits a differentiable version of the Dynamic Time Warping (DTW) similarity – referred to as soft-DTW divergence – as loss function for a flexible neural network architecture. This is a different paradigm with respect to state-of-the-art approaches in which sEMG-based control of robot hands is mainly realized using supervised or unsupervised machine learning based regression. An experimental session was carried out involving 10 healthy subjects in an offline experiment for systematic and statistical evaluations, and an online experiment for the evaluation of the control of a robot hand. The reported results demonstrate that the proposed soft-DTW neural network can be trained by means of a labelling that does not require to be temporally aligned with the sEMG training dataset, while reporting performances comparable with a standard mean square error(MSE)-based neural network. Also, the subjects were able to successfully control a robot hand for grasping motions and tasks with error levels comparable to state-of-the-art regression approaches.

In this paper, an interactive robotic anthropomorphic hand system is presented, which was developed as an important tool for the educational outreach activities. The robotic anthropomorphic hand incorporates 15 degrees of freedom, providing sufficient mobility in the demonstration of various postures. To increase the attractiveness of the robotic hand, pneumatic muscle actuators are used to drive the robotic hand motion through artificial tendons. The interaction of the robotic hand with a human is enabled with a control device, which allows the human operator to control the hand motion in a natural way. The robotic hand system has been successfully demonstrated in a recent engineering education outreach event, in which over 100 children at all ages operated the robotic hand through the control device.

In recent years, the demands of using robotic hands have been increased dramatically in a variety of applications such as intelligent manufacturing. The current robotic hands usually are functioned relying on the assembly of numerous components, which bring unnecessary dead weight and complex control systems. Such shortcomings limit the further application of robotic hands. To address this issue, this paper presents a lightweight and low-complexity five-fingered robotic hand called the Integrated Finger Robotic Hand (IFRH). Each finger of the robotic hand is built as an integrated object based on a compliant joint called the Elastic Knuckle Connection (EKC). The 3D-printed finger design minimizes the number of components as well as the weight of the system. The IFRH can be assembled quickly and maintained easily due to its low number of parts compared to traditional separate-finger robotic hands. The IFRH is driven by servo motors which transmit motion to a monofilament fishing string. This fishing string is connected to each finger, which mirrors the grasping motion of a real human hand when contracted. With feedback from force sensors, the IFRH accomplishes the goal of high precision grasping of daily objects. The IFRH can be controlled by a potentiometer powered by Arduino, further increasing the user-friendly nature of the robotic hand. A number of experimental tests are conducted to verify the accuracy of the force transmission system, the grasping capabilities, and the grasping precision.

The goal of this study is to develop a wireless-controlled robotic hand that is capable of replicating the movements of a human hand. A central objective of the design process is the development of a sensing technique that can accurately capture and translate human hand movements into electrical signals in a sustainable manner. This sensing technology relies on the detection of flexion and extension in the human hand, achieved through the modulation of light transmitted from a Light-Emitting Diode (LED) and received by a Light-Dependent Resistor (LDR). Five sensors, each corresponding to a finger, are integrated into a glove worn by the user, thereby enabling an intuitive control of the robotic hand. The sensors generate discrete electrical signals with each finger movement, which are then wirelessly transmitted via nRF24l01 modules to the microcontroller of the robotic hand. Subsequently, the microcontroller generates the requisite electrical signals to actuate the servo motors, thereby orchestrating the movement of the robotic fingers to mimic human hand gestures. The robotic hand, comprising 46 individual components fabricated using biodegradable polylactic acid material via 3D printing, successfully achieves its objective of replicating human hand movements. However, a minor delay in milliseconds is observed between human hand movements and the corresponding robotic hand movements. Despite this delay, the developed system shows promise for applications in hazardous environments, such as dangerous chemistry experiments, and enhances safety in the medical laboratory. It may also play a crucial role in remote surgery procedures.

Grasping unknown objects with multi-fingered hands is challenging due to incomplete information regarding scene geometry and the complicated control and planning of robot hands. We propose a method for grasping unknown objects with multi-fingered hands based on shape complementarity between the robot hand and the object. Taking as input a point cloud of the scene we locally perform shape completion and then we search for hand poses and finger configurations that optimize a local shape complementarity metric. We validate the proposed approach in MuJoCo physics engine. Our experiments show that the explicit consideration of shape complementarity of the hand leads to robust grasping of unknown objects.

Grasp control refers to the art of controlling the motion of an object by constraining its dynamics through contacts with a hand. The process of controlling the grasp is not limited to robotic hands only but also applies to human hands (Johansson and Edin 1991) and to all other mechanisms using contact constraints to control the motion of the manipulated object (Brost and Goldberg 1996). A crucial role in the control of grasping is played by contact constraints. All the interactions between the robotic hand and the grasped object occur at the contacts whose understanding is paramount (Salisbury and Roth 1983). The unilateral nature of contact interaction in grasping makes the control problems much more challenging than cooperative manipulation where multiple arms hold the object rigidly allowing bilateral force transmission at each contact point (Chiacchio et al. 1991). The importance of unilateral contact constraints in grasping led a large part of the literature to focus on the closure properties of the grasp (Bicchi 1995). Those properties refer to the ability of a grasp to prevent motions of the grasped object relying only on unilateral frictionless constraints in case of form closure (Reuleaux 1876) and on contact constraints with friction in case of force closure (Nguyen 1988). While form closure is a purely geometric property of the grasp and depends on where the unilateral contact points are on the object, force closure depends on the ability that the robotic hand has to resist and apply forces to the object through the contacts while satisfying the friction constraints. In other terms force closure directly involves the control of the robotic hand kinematics and not only the geometry of the contacts (Bicchi 1995). This entry focuses on force-closed grasps. The optimal choice of the contact points on the object surface is a critical issue known as grasp planning. Among the many optimal criteria that have been proposed in the literature to choose the contact points, I want to recall the one proposed in Ferrari and Canny (1992) where the grasping configuration is evaluated according to the magnitude of the largest worst-case disturbance wrench that can be resisted by the grasp. Many approaches have been studied in the literature on grasp planning in the presence of uncertainties. The uncertainty can be either due to the shape of the object which is partially known or partially sensed as in Goldfeder et al. (2009) or due to the errors in positioning the fingers on the object during the grasping (Roa and Suarez 2009). In what follows all the parameters of the grasp including those related to the hand, the object, and the contact points are assumed to be known with no uncertainties. The main objective of grasp control is that of tracking a desired trajectory with the grasped object by applying a set of contact forces satisfying the friction constraints (Bicchi and Kumar 2000). Complex in-hand object motions can be obtained by rolling and sliding the contact points on the object surface as proposed in Montana (1988) or by using finger gaiting to get large-scale motions (Han and Trinkle 1998). This entry deals with non-rolling and non-sliding contact points