  High-speed and high-acceleration movements are inherently hard to control.
Applying learning to the control of such motions on anthropomorphic robot arms
can improve the accuracy of the control but might damage the system. The
inherent exploration of learning approaches can lead to instabilities and the
robot reaching joint limits at high speeds. Having hardware that enables safe
exploration of high-speed and high-acceleration movements is therefore
desirable. To address this issue, we propose to use robots actuated by
Pneumatic Artificial Muscles (PAMs). In this paper, we present a four degrees
of freedom (DoFs) robot arm that reaches high joint angle accelerations of up
to 28000 deg/s^2 while avoiding dangerous joint limits thanks to the
antagonistic actuation and limits on the air pressure ranges. With this robot
arm, we are able to tune control parameters using Bayesian optimization
directly on the hardware without additional safety considerations. The achieved
tracking performance on a fast trajectory exceeds previous results on
comparable PAM-driven robots. We also show that our system can be controlled
well on slow trajectories with PID controllers due to careful construction
considerations such as minimal bending of cables, lightweight kinematics and
minimal contact between PAMs and PAMs with the links. Finally, we propose a
novel technique to control the the co-contraction of antagonistic muscle pairs.
Experimental results illustrate that choosing the optimal co-contraction level
is vital to reach better tracking performance. Through the use of PAM-driven
robots and learning, we do a small step towards the future development of
robots capable of more human-like motions.

Robotics;Machine Learning;Systems and Control