The Influence of the Abduction Joints of Four Fingers to Grasp: Experimental and Simulated Verification

The thumb is the most important finger of the human hand and has a great influence on grasp manipulations. However, the extent to which joints other than the thumb joints affect the grasp, and thus, which joints should be included in a prosthetic hand, remains an open issue. In this paper, we focus on the metacarpophalangeal joints of the four fingers, except the thumb, which can generate flexion/extension and abduction/adduction motions. The contribution of these joints to grasping was evaluated in four aspects: grasp size, grasp force, grasp quality and grasp success rate. Six subjects participated in experiments with respect to the maximum grasp size and grasp force. The results show that possessing abduction mobility of the metacarpophalangeal joints can increase the grasp size by 4.67 ± 1.93 mm and the grasp force by 5.27 ± 4.25 N. Then, the grasping quality and success rate were tested in a simulation platform and using a robotic hand, respectively. The results show that grasp quality was promoted by 76.7% in the simulated environment with abduction mobility compared to without abduction mobility, whereas the grasp success rate was promoted by 68.3%. We believe that the results of this work can benefit the understanding of hand function and prosthetic hand design.

Optimal grasp force for robotic grasping and in-hand manipulation with impedance control

Purpose The purpose of this paper is to achieve stable grasping and dexterous in-hand manipulation, the control of the multi-fingered robotic hand is a difficult problem as the hand has many degrees of freedom with various grasp configurations. Design/methodology/approach To achieve this goal, a novel object-level impedance control framework with optimized grasp force and grasp quality is proposed for multi-fingered robotic hand grasping and in-hand manipulation. The minimal grasp force optimization aims to achieve stable grasping satisfying friction cone constraint while keeping appropriate contact forces without damage to the object. With the optimized grasp quality function, optimal grasp quality can be obtained by dynamically sliding on the object from initial grasp configuration to final grasp configuration. By the proposed controller, the in-hand manipulation of the grasped object can be achieved with compliance to the environment force. The control performance of the closed-loop robotic system is guaranteed by appropriately choosing the design parameters as proved by a Lyapunove function. Findings Simulations are conducted to validate the efficiency and performance of the proposed controller with a three-fingered robotic hand. Originality/value This paper presents a method for robotic optimal grasping and in-hand manipulation with a compliant controller. It may inspire other related researchers and has great potential for practical usage in a widespread of robot applications.

Grasping Assisting Algorithm in Tele-Operated Robotic Gripper

The involvement of Robots and automated machines in different industries has increased drastically in recent years. Part of this revolution is accomplishing tasks previously performed by humans with advanced robots, which would replace the entire human workforce in the future. In some industries the workers are required to complete different operations in hazardous or difficult environments. Operations like these could be replaced with the use of tele-operated systems that have the capability of grasping objects in their surroundings, thus abandoning the need for the physical presence of the human operator at the area while still allowing control. In this research our goal is to create an assisting system that would improve the grasping of a human operator using a tele-operated robotic gripper and arm, while advising the operator but not forcing a solution. For a given set of objects we computed the optimal grasp to be achieved by the gripper, based on two grasp quality measures of our choosing (namely power grasp and precision grasp). We then tested the performance of different human subjects who tried to grasp the different objects with the tele-operated system, while comparing their success to unassisted and assisted grasping. Our goal is to create an assisting algorithm that would compute optimal grasps and might be integrated into a complete, state-of-the-art tele-operated system.

Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback

How humans visually select where to grasp objects is determined by the physical object properties (e.g., size, shape, weight), the degrees of freedom of the arm and hand, as well as the task to be performed. We recently demonstrated that human grasps are near-optimal with respect to a weighted combination of different cost functions that make grasps uncomfortable, unstable, or impossible, e.g., due to unnatural grasp apertures or large torques. Here, we ask whether humans can consciously access these rules. We test if humans can explicitly judge grasp quality derived from rules regarding grasp size, orientation, torque, and visibility. More specifically, we test if grasp quality can be inferred (i) by using visual cues and motor imagery alone, (ii) from watching grasps executed by others, and (iii) through performing grasps, i.e., receiving visual, proprioceptive and haptic feedback. Stimuli were novel objects made of 10 cubes of brass and wood (side length 2.5 cm) in various configurations. On each object, one near-optimal and one sub-optimal grasp were selected based on one cost function (e.g., torque), while the other constraints (grasp size, orientation, and visibility) were kept approximately constant or counterbalanced. Participants were visually cued to the location of the selected grasps on each object and verbally reported which of the two grasps was best. Across three experiments, participants were required to either (i) passively view the static objects and imagine executing the two competing grasps, (ii) passively view videos of other participants grasping the objects, or (iii) actively grasp the objects themselves. Our results show that, for a majority of tested objects, participants could already judge grasp optimality from simply viewing the objects and imagining to grasp them, but were significantly better in the video and grasping session. These findings suggest that humans can determine grasp quality even without performing the grasp—perhaps through motor imagery—and can further refine their understanding of how to correctly grasp an object through sensorimotor feedback but also by passively viewing others grasp objects.

Humans can visually judge grasp quality and refine their judgments through visual and haptic feedback

How humans visually select where to grasp objects is determined by the physical object properties (e.g., size, shape, weight), the degrees of freedom of the arm and hand, as well as the task to be performed. We recently demonstrated that human grasps are near-optimal with respect to a weighted combination of different cost functions that make grasps uncomfortable, unstable or impossible e.g., due to unnatural grasp apertures or large torques. Here, we ask whether humans can consciously access these rules. We test if humans can explicitly judge grasp quality derived from rules regarding grasp size, orientation, torque, and visibility. More specifically, we test if grasp quality can be inferred (i) by using motor imagery alone, (ii) from watching grasps executed by others, and (iii) through performing grasps, i.e. receiving visual, proprioceptive and haptic feedback. Stimuli were novel objects made of 10 cubes of brass and wood (side length 2.5 cm) in various configurations. On each object, one near-optimal and one sub-optimal grasp were selected based on one cost function (e.g. torque), while the other constraints (grasp size, orientation, and visibility) were kept approximately constant or counterbalanced. Participants were visually cued to the location of the selected grasps on each object and verbally reported which of the two grasps was best. Across three experiments, participants could either (i) passively view the static objects, (ii) passively view videos of other participants grasping the objects, or (iii) actively grasp the objects themselves. Our results show that participants could already judge grasp optimality from simply viewing the objects, but were significantly better in the video and grasping session. These findings suggest that humans can determine grasp quality even without performing the grasp—perhaps through motor imagery—and can further refine their understanding of how to correctly grasp an object through sensorimotor feedback but also by passively viewing others grasp objects.

Reach to Grasp Planning for a Synergy-Controlled Robotic Hand based on Pesudo-Distance Formulation

In the past several years, grasp analysis of multi-fingered robotic hands has been actively studied through the use of posture synergies. In these grasping planning algorithms, a formulated optimization is usually performed in the hand’s low-dimensional representation together with the hand’s position and orientation. The optimization terminates at a stable grasp, often after repeated trials with different initial guesses. Furthermore, there is no guarantee that the generated grasp leads to a smooth reach-to-grasp trajectory since the grasping planning process mostly concerns hand poses with the fingers proximal to the object. A unified theoretical framework of a gradient-based iterative algorithm is hence proposed in this paper to plan a reach-to-grasp task, predicting the grasp quality and adjusting the hand’s posture synergies, position and orientation during the approaching phase to achieve a stable grasp. The grasp quality measurement is adopted from a highly efficient pseudo-distance formulation. Stable power grasp and precision pinch can be consistently and intentionally planned with different contact conditions specified in the formulation, which means that an intention for planning a power grasp would not generate a pinch result. Several numerical simulation case studies are presented to demonstrate the effectiveness of the proposed algorithm.