Yasunori Tada

Anthropomorphic Robotic Soft Fingertip with Randomly Distributed Receptors

Abstract To improve the manipulation ability of robotic fingers, this paper proposes a design of an anthropomorphic soft fingertip with distributed receptors. The fingertip consists of two silicon rubber layers of different hardness containing two kinds of receptors, strain gauges and PVDF (polyvinylidene fluoride) films. The structure of the fingertip is similar to that of a human’s; it consists of a bone, a body, a skin layer, and randomly distributed receptors inside. Experimental results demonstrate the discriminating ability of the fingertip: it can discriminate five different materials by pushing and rubbing the objects.

Sensing the texture of surfaces by anthropomorphic soft fingertips with multi-modal sensors

This paper describes the development of a human-like multi-modal soft finger and its ability to sense the texture of objects. This fingertip has two silicon rubber layers of different hardness; strain gauges and PVDF films are randomly distributed as tactile sensors. Owing to the dynamics of the silicon between sensors, the fingertip is supposed to have several sensor modalities. Preliminary experiments show that the fingertip can detect the difference between the textures of objects (paper and wood).

A flexible and stretchable tactile sensor utilizing static electricity

The tactile sensor is required for various robots. In humanoid robots, flexibility of the sensor is an important feature for preventing physical damage and for interacting with the human. Moreover, stretchability of the sensor has advantages that the sensor is nonbreakable and that the sensor can be easily mounted on curved surfaces or deformable parts such as joints. This paper proposes a novel tactile sensor made of flexible and stretchable silicone rubber. A structure of the sensor is similar to the capacitive tactile sensors. However, the proposed sensor utilizes a different principle from existing sensors. The sensor utilizes static electricity and electrostatic induction phenomenon, and can detect some touch conditions. This paper reports the principle and characteristics of the proposed sensor. Experiments show that the sensor output depends on touch area, touch velocity, and material of touch objects. However, the sensor does not depend on touch weight. Moreover, the experiment shows that even if the proposed sensor is stretched, it performs as the tactile sensor.

Acquisition of multi-modal expression of slip through pick-up experiences

To realize adaptive and robust manipulation, a robot should have several sensing modalities and coordinate their outputs to achieve the given task based on underlying constraint in the real environment. This paper discusses on acquisition of multi-modal expression of slip consisting of vibration, pressure, and vision sensations through pick-up experiences. A sensor network is proposed to acquire the expression, whose learning ability is demonstrated by a real experiment. The applicability of the learned network is also demonstrated by experiments to realize adaptive picking

Learn to grasp utilizing anthropomorphic fingertips together with a vision sensor

A robot should have softness and many sensors to manipulate an object dexterously and to adapt various environments. However, many existing schemes where a designer calibrates the sensor output to the world coordinate frame are difficult to adapt for such the robot. This paper proposes a learning mechanism for a robot hand which consists of anthropomorphic fingertips. The sensor for the fingertip is difficult to calibrate because the sensor receptors are embedded randomly in the soft material. The effectiveness of the proposed mechanism is demonstrated by an experiment that the robot picks up an unknown weight object.

Acquisition of Multi-Modal Expression of Slip through Pick-Up Experiences

To realize adaptive and robust manipulation, a robot should have several sensing modalities and coordinate their outputs to achieve the given task based on underlying constraint in the real environment. This paper discusses on acquisition of multi-modal expression of slip consisting of vibration, pressure, and vision sensations through pick-up experiences. A sensor network is proposed to acquire the expression, whose learning ability is demonstrated by a real experiment. The applicability of the learned network is also demonstrated by experiments to realize adaptive picking.

1A1-B29 Skill Acquisition of Peg-in-Hole Task Using Anthropomorphic Soft Fingers

An anthropomorphic soft finger with many tactile receptors is supposed to have high tactile sensing ability. However, it is more difficult to control a robot equipped with the fingertip because it is too difficult for the designer to calibrate all the receptors randomly distributed in it. One of the approaches to realize dexterous manipulation by using it is skill acquisition through a task. In this paper, we propose a learning mechanism to acquire a skill for Pegin-Hole task by using tactile sensor and vision sensor. We show a preliminary experimental result to show the effectiveness of the proposed method.