Robots can be made from soft materials, but the flexibility of such robots is limited by the inclusion of rigid sensors necessary for their control.
The relationship between machine learning and robotics is not just limited to the behavioral control of robots, but is also important for their design and core functions.
"Take for example a robot with pneumatic artificial muscles (PAM), rubber and fiber-based fluid-driven systems which expand and contract to move," said Associate Professor Kohei Nakajima from the Graduate School of Information Science and Technology.
Accurate laser-based monitors help maintain control through feedback, but these rigid sensors restrict a robot's movement, so we came up with something new."
This is where information about a system, in this case the PAM, is fed into a special artificial neural network in real time, so the model is ever changing and thus adapts to the environment.