  For locomotion, is an arm on a legged robot a liability or an asset for
locomotion? Biological systems evolved additional limbs beyond legs that
facilitates postural control. This work shows how a manipulator can be an asset
for legged locomotion at high speeds or under external perturbations, where the
arm serves beyond manipulation. Since the system has 15 degrees of freedom
(twelve for the legged robot and three for the arm), off-the-shelf
reinforcement learning (RL) algorithms struggle to learn effective locomotion
policies. Inspired by Bernstein's neurophysiological theory of animal motor
learning, we develop an incremental training procedure that initially freezes
some degrees of freedom and gradually releases them, using behaviour cloning
(BC) from an early learning procedure to guide optimization in later learning.
Simulation experiments show that our policy increases the success rate by up to
61 percentage points over the baselines. Simulation and real robot experiments
suggest that our policy learns to use the arm as a tail to initiate robot
turning at high speeds and to stabilize the quadruped under external
perturbations. Quantitatively, in simulation experiments, we cut the failure
rate up to 43.6% during high-speed turning and up to 31.8% for quadruped under
external forces compared to using a locked arm.
