MEMO Technos' collaborative robot "Loux Pro".

MEMO Technos' collaborative robot "Loux Pro".

The robot for the work section can be changed according to the user's specifications. The company aims to receive orders for 10 systems a year. The size of the robot is approximately 795 mm (width), 1405 mm (height) and 710 mm (depth). It weighs 230 kilograms. It has a payload of 15 kilograms. On a full charge, the arm part can operate for one hour and the mobile part for 13 hours. The robot can move without any track or markings and automatically connects to the charging device when needed. The twin-armed robot of the working part has a total of seven axes, and teaching work is relatively easy because it has a direct teaching function that teaches movement by holding the arm. Since the robot can work together with a person, it can be used to help pack boxes beside a worker, or to take the place of a worker when a vacancy occurs. (Photo taken on March 2, 2020, location unknown, credit: Nikkan Kogyo Shimbun / Kyodo News Images)

Dressing and undressing assistance robot at Kyushu Institute of Technology Graduate School

Dressing and undressing assistance robot at Kyushu Institute of Technology Graduate School

These cases are stored as a learning database. The robot will be able to respond to the differences between each person and each piece of clothing. The robot is a twin-armed robot with a payload of 2.3 kilograms, and it is a cooperative type, so there is no risk of injury to people even if it bumps into them while working. The placement of a camera that can respond to the sudden movement of a person is an issue, but Professor Shibata says, "In large facilities or places where the changing room can be fixed, such as hospitals, the camera can be installed on the ceiling. They aim to make improvements and put the system to practical use as soon as possible. (= December 20, 2019, location unknown, credit: Nikkan Kogyo Shimbun / Kyodo News Images)

A twin-armed robot by Professor Tetsuya Ogata and his colleagues at Waseda University

A twin-armed robot by Professor Tetsuya Ogata and his colleagues at Waseda University

Professor Tetsuya Ogata and his teamat Waseda University have developed a technology that allows a deep learning model to learn and perform multiple actions. They demonstrated the function by applying it to a twin-armed robot folding towels.In the case of folding towels, four motions were learned, including folding the towel from the edge and repositioning the towel. We trained the deep learning model so that the internal state of the model is the same at the beginning and end of each action. For each of the four actions, we used a large deep learning model with about 300 cells and 400 steps. In this study, we learned multiple actions by connecting them to a single model. In the future, we will apply the model to complex operations such as skewering to verify the learning method suitable for each operation. The technology for using deep learning to identify images is already in the practical stage. Photo taken on October 16, 2019, credit: Nikkan Kogyo Shimbun / Kyodo News Images

Okinawa Industry Association's Experience Event for Children

Okinawa Industry Association's Experience Event for Children

Can you beat a robot? The Okinawa Industrial Association (Naha City, Okinawa, 098-859-6191, Chairman Noboru Kohatsu, Chairman of Takunan Iron & Steel) promoted industrial robots through a hands-on event for children. In this game, children faced a cooperative twin-armed robot and arranged 10 pieces in a row. The game started with a nod from the robot's "head," and the children competed for speed with all their might. There have been few cases of robots being introduced in Okinawa, so the youth division of the association held this event at the "Okinawa Industry Festival" exhibition with the aim of promoting the spread of robots and understanding of manufacturing. Kasai Elec (Oguchi-cho, Aichi Prefecture), a system integration company, cooperated in the event. Photo taken on October 26, 2019, credit: Nikkan Kogyo Shimbun / Kyodo News Images