Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : May 27, 2020 - May 30, 2020
Recent robots have been used in various environments and require complicated movements according to various environment. It is difficult to control the robot in every environmental situation, however, it is possible for a robot to learn the given motion by using machine learning techniques that imitates the infant's motor learning. It is expected that the dynamical properties, such as inertia, reaction forces, internal forces flow could be one of the constraints which support to reduce the exploring space. To investigate the relationship between an effectiveness of machine learning and constraints induced by body-dynamics, we developed the whole systems including tendon-driven legs robot, split-belt treadmill, and hip joint with a six-axis sensor to measure how a multi-legged animal affected itself and the environment during walking. It is confirmed that it was possible to measure the data around the hip joint during walking in experiments.
