In addition, Fujitsu simultaneously developed a software program, known as the Humanoid Movement-Generation System, which enables humanoid robots to learn a wide range of movements.įigure 2 shows the neural network and the full-body movements generated by the new method.
This combination, known as CPG/NP learning, is optimized in the new technology. This is combined with a Numerical Perturbation Method (NP) ( 5) that quantifies the configuration and connection-weight status of the network.
#Humanoid prototype 3 generator#
About the Technologyįujitsu's new technology is based on Central Pattern Generator (CPG) ( 3) networks, which mathematically simulate the neural oscillator ( 4) found in vertebrates. But neural networks have so far proven to be very slow in learning movements, requiring days or even months, and they have been unable to generate a variety of motions efficiently. One method of resolving this problem that drew considerable attention was the use of a neural network based control system, which mimics the way living things learn. Moreover, it was thought that an even higher computing power was required to enable robots to respond instantly to changing situations. Until now, motion generation and control for a humanoid robot involved very complex calculations of dynamics, requiring considerable computing power. In the not-too-distant future, robots are expected to live alongside humans and handle many domestic tasks.
In recent years, domestic robots ( 2) have been a key area of research in the field of robotics, especially humanoid robots. In addition, this work was presented earlier this month at the Robotics Symposia in Shizuoka, Japan. This achievement is a significant leap forward in the development of humanoid robots, making the generation of motion in a humanoid robot, for which complex controls are required, a dramatically faster and simpler process.ĭetails on this system will be made public at ROBODEX2003, held at Pacifico Yokohama (Japan) from April 3 to 6. today announced that it has developed the world's first learning system for humanoid robots that uses a dynamically reconfigurable neural network ( 1) to enable the efficient learning of movement and motor coordination.