When a disaster occurs, high output power should be available for rescue operation even if the electric supply is insufficient at site. This video presents a novel multi-fingered robot hand for extreme environments without a sufficient electric supply. The robot hand has four fingers with 16 joints and 12 degrees of freedom. The finger has a retention mechanism using no electrical power supply and a fingertip force of 150 [N]. Holding without power supply shows that our robot hand can lift a heavy barbell and keep its posture without using electrical power. The high fingertip force shows that steel cans can be crushed by the robot hand. In addition, dexterous motion of our robot hand shows that each finger allows flexion/extension and adduction/abduction. High-power manipulation shows that the robot hand can grasp and manipulate a hammer drill for making a hole in a concrete plate. The robot hand has a high potential for performing various tasks by obtaining high power output and electrical power saving.

This paper presents a novel learning system for myoelectric prosthetic hand control by forearm amputees using estimations of continuous joint angles. Wavelengths calculated using surface electromyogram (sEMG) signals of forearm amputees are input into a neural network (NN); past inputs are also used to take finger dynamics into consideration when estimating the metacarpophalangeal joint angles of each finger and wrist joint angle of pronation/supination and palmar flexion/dorsiflexion. The learning system has a three-step learning dataset generation process: (1) continuous motion of a virtual prosthetics hand (VR-hand) and motion timing bar are displayed to a subject; (2) the subject contracts his/her muscles following the VR-hand motion; and (3) sEMG signals and joint angles of VR-hand are measured and saved as the learning dataset. This system does not need to measure actual joint angles. To demonstrate the effectiveness of this learning system, RMS error of joint angle estimations are presented in cases of a motion set with 8 patterns for a healthy subject, and a motion set with 4 patterns for a right forearm amputee.

Students studying to become therapists have few opportunities for repeated training for the rehabilitation of contracture joints. This paper proposes the concept of a robot hand system for repeated rehabilitation training. A novel robot hand and artificial skin are developed in collaboration with doctors and therapists. Development of the robot hand is based on new design concepts aimed at imitating a human hand with a disability. The joint torque of a disabled person can be estimated by distributed tactile sensors. A model of contracture joints with tendon adhesion is introduced. The robot hand in imitation of contracture joints is governed by the force control based on torque control. The effectiveness of the proposed method is demonstrated experimentally.

This paper presents the design and control of a newly developed five-fingered haptic interface robot named HIRO II. The developed haptic interface can present force and tactile feeling to the five fingertips of the human hand. Its mechanism consists of a 6 degree of freedom (DOF) arm and a 15 DOF hand. The interface is placed opposite the human hand, which ensures safety and freedom of movement, but this arrangement leads to difficulty in designing and controlling the haptic interface, which should accurately track the fingertip positions of the operator. A design concept and optimum haptic finger layout, which maximizes the design performance index is presented. The design performance index consists of the product space between the operator's finger and the hapic finger, and the opposability of the thumb and fingers. Moreover, in order to reduce the feeling of uneasiness in the operator, a mixed control method consisting of a finger-force control and an arm position control intended to maximize the control performance index, which consists of the hand manipulability measure and the norm of the arm-joint angle vector is proposed. The experimental results demonstrate the high potential of the multifingered haptic interface robot HIRO II+utilizing the mixed control method.

Haptic interfaces presenting force and tactile feeling at human fingertips are used in the area of telemanipulation of robots, simulation and design in virtual reality environments, educational training, and so on. Multi-fingered haptic interface is required to be safe, to work in wide operation space, and to present not only force at contact points but also weight feeling of virtual objects, to have no oppressive feeling when it is attached to humans, and to have no weight feeling of itself. The paper presents two control methods for the multifingered haptic interface, which is a redundant robot. First is redundant force control, which takes into account moments at tip of an interface arm. The second is a combination of the force control of the fingers and the position control of the arm, which optimizes the manipulability of haptic hand and minimizes variation of joint angles of the interface arm. Experimental results of free space and constrained space are shown. The control methods are well suited not only for the multi-fingered haptic interface but also for the multi-fingered robot hand for grasping and manipulating an object

This paper presents a newly developed anthropomorphic robot hand called KH Hand type S, which has high potential of dexterous manipulation and displaying hand shape, and its master slave system using the bilateral controller for five-fingers robot hand. The robot hand is improved by reducing the weight, the backlash of transmission, and the friction between gears by using elastic body. Expression of Japanese finger alphabet is shown. In order to demonstrate the dexterous grasping and manipulating an object, the experiment of peg-in-hole task controlled by bilateral controller is shown. These results denote that the KH Hand type S has a high potential to perform dexterous object manipulation like the human hand.

Haptic interfaces, which present force and tactile feeling at human fingertips, are utilized in the aria of tele-manipulation of robots, simulation and design in virtual reality environments, educational training, and so on. The haptic interface is demanded to be safe, to work in wide operation space, and to present not only force at contact points but also weight feeling of virtual objects, to have no oppressive feeling when it is attached to humans, and to have no weight feeling of itself. This paper presents a control architecture of the developed multi-fingered haptic interface, named Gifu Haptic Interface. The haptic interface was designed to be completely safe and to be similar to the human upper limb in shape and motion ability. The interface is placed opposed to the human hand, which brings safety and no oppressive feeling, but causes difficulty in controlling the haptic interface because it should follow the hand poses of the operator. Two control methods of the haptic fingers are tested and two approaches to oppose the interface hand to the human hand are studied. A computer graphics simulation and experiments are also presented.

This paper proposes an algorithm for identification of contact conditions between a grasped object and external environment from position and velocity information of a robot hand. The contact conditions mean contact type, contact position, and contact force. Soft finger contact type, line contact type, and plane contact type are considered in addition to point contact type. The contact type is identified by rank of the matrix that places estimated angular velocity of the object at contact point. Contact position, contact normal and contact line are also estimated. This method has the following merits as compared with the method using 6-axes force information described in the previous paper. The identification problem is reduced to the problem of solving linear equations. The required number of active sensing motion is less.

A virtual robot teaching that consists of human demonstration and motion-intention analysis in a virtual reality environment is an advanced technology of automatic programming for multi-fingered robots. For the virtual hand model displayed on-screen, a human-hand model is better than a robot-hand model in terms of teaching time and a stable manipulation of virtual object. However, it may occurs that a robot cannot grasp an object at a teaching position and orientation of the robot hand because the geometrical size and motional function of the robot hand is not the same as that of human hand. To solve this problem, we propose a virtual teaching based on hand manipulability, in which a position and orientation of the robot hand is determined so as to maximize a manipulability of the robot hand on the condition that the robot grasps the object at the teaching contact points on the object. Experimental results of a pick-and-place task are shown to demonstrate the effectiveness of the proposed method.

This paper discusses a method for identification of contact conditions from the information of 6-axes force sensor equipped with a robot hand. The previous paper (1999) has the following problems: the noise of sensing force was not considered, and hence the estimates obtained by the previous method are biased from true value, and the identification of contact types depends on an unknown contact position. In this paper we consider the noise of force. We propose a method of removing the bias from the estimates. Hence, it is guaranteed that asymptotically unbiased estimates are obtained. The contact types can be judged by eigenvalues of a covariance matrix, which is derived from estimates of the contact moment. The effectiveness of the algorithm is demonstrated by simulations.

When a grasped object is in contact with external environment, it is required to identify contact conditions prior to performing the assembly tasks. This paper discusses a method for identification of contact conditions from the information of force sensor equipped with the robot hand. This paper treats the practical case where sensing data are contaminated with noise. We propose an efficient and analytical algorithm for identifying contact conditions by using an active force sensing method. The algorithm can identify not only contact position and contact force, but also contact type such as soft finger contact type, line contact type, and plane contact type. These contact types are characterized by a standard deviation of contact moments. The contact position is estimated by a least-squares method. The contact moment is then estimated from noisy observations and its eigenvalues are analyzed. The contact type can be judged by the eigenvalues of estimated contact moment. The effectiveness of the algorithm is demonstrated by simulations.