  This study is aimed at addressing the problem of fault tolerance of quadruped
robots to actuator failure, which is critical for robots operating in remote or
extreme environments. In particular, an adaptive curriculum reinforcement
learning algorithm with dynamics randomization (ACDR) is established. The ACDR
algorithm can adaptively train a quadruped robot in random actuator failure
conditions and formulate a single robust policy for fault-tolerant robot
control. It is noted that the hard2easy curriculum is more effective than the
easy2hard curriculum for quadruped robot locomotion. The ACDR algorithm can be
used to build a robot system that does not require additional modules for
detecting actuator failures and switching policies. Experimental results show
that the ACDR algorithm outperforms conventional algorithms in terms of the
average reward and walking distance.
