  We have developed a parallel wire-driven monopedal robot, RAMIEL, which has
both speed and power due to the parallel wire mechanism and a long acceleration
distance. RAMIEL is capable of jumping high and continuously, and so has high
performance in traveling. On the other hand, one of the drawbacks of a minimal
parallel wire-driven robot without joint encoders is that the current joint
velocities estimated from the wire lengths oscillate due to the elongation of
the wires, making the values unreliable. Therefore, despite its high
performance, the control of the robot is unstable, and in 10 out of 16 jumps,
the robot could only jump up to two times continuously. In this study, we
propose a method to realize a continuous jumping motion by reinforcement
learning in simulation, and its application to the actual robot. Because the
joint velocities oscillate with the elongation of the wires, they are not used
directly, but instead are inferred from the time series of joint angles. At the
same time, noise that imitates the vibration caused by the elongation of the
wires is added for transfer to the actual robot. The results show that the
system can be applied to the actual robot RAMIEL as well as to the stable
continuous jumping motion in simulation.
