This paper proposes a pair of single or multi-DOF robot fingers with soft and deformable tips that can pinch an object stably in a dynamic sense with the aid of real-time sensory feedback. To realize dynamic stable pinching, a practical method of using optical devices is proposed for measuring both the maximum displacement of finger-tip deformation and the relative angle between the object surface and each of finger links. It is shown that the overall closed-loop system of a pair of two single-DOF fingers with soft tips with real-time sensory feedback of the difference between the centers of two area-contacts at both sides of the object becomes asymptotically stable. This means that the pair achieves dynamic stable grasping (pinching). In the case of a pair of 1-DOF and 2-DOF fingers with soft tips, it is shown that the proposed method of closed-loop feedback of the difference between the centers of two area-contacts and the rotational angle of the object can establish not only dynamic stable grasping but also regulation of the posture of the object.

This paper proposes an iterative learning control scheme for impedance control of robotic tasks when the tool endpoint covered by soft and deformable material presses a rigid object or environment at a prescribed periodic force pattern. To this end, an iterative learning control scheme for a class of linear dynamical systems with a negative feedback structure is analyzed and convergence of the proposed learning update law after a sufficient number of repetitions is proved. It is shown that this convergence realizes impedance matching in a sense of electric circuit theory of the feedback system can be expressed as a lumped-parameter electric circuit. The iterative learning control scheme is then applied for a case of impedance control of robotic tasks when the characteristics of reproducing force of the deformable material is nonlinear in its displacement and unknown and the tool mass is uncertain. Simulation results are also presented, which show effectiveness of the proposed learning control scheme.

This paper aims to generalize the concept of impedance matching to cope with nonlinear dynamics that govern robotic systems with or without constraints. In the case of single degree of freedom dynamics the impedance matching is first introduced by the concept of balancing between load impedance and internal impedance. Generalization of this concept to the case of multiple degrees of freedom robots can be fulfilled implicitly via a coordinates transformation by constructing a negative feedback connection of two hyper-stable blocks (one is a parameter estimator and positioning and the other is a position and force regulator).

Most research on multifingered robot control has assumed that the Jacobian matrices from joint space to task space is exactly known. This implies that the locations of contact points, geometry of the object, kinematics of the multifingered robot hands must be exactly known. In this paper, a task-space feedback control problem of multifingered robot hands with uncertain Jacobian matrices is formulated and solved. The stability and robustness of the proposed controllers to the uncertainties in Jacobian matrices are analyzed.

In many cases of human hand tasks, grasping and manipulation of objects are the most basic motions. However, the influence of physical characteristics of human finger on grasping motions have not yet been sufficiently clarified. For example, friction which is an important characteristic of grasping motions has hardly been investigated. In this study, we pay attention, to friction between a finger and an object. First, we develop a system to measure the friction on fingers. Next, based on the experimental results obtained from the measurement system, we propose a new model for the maximum static friction force on human fingers. Also, we find that the friction forces are changed depending on the directions of tangential forces. Finally, we realize a finger-tip which is made of silicone and whose characteristics are similar to those of human fingers.