  Deep reinforcement learning produces robust locomotion policies for legged
robots over challenging terrains. To date, few studies have leveraged
model-based methods to combine these locomotion skills with the precise control
of manipulators. Here, we incorporate external dynamics plans into
learning-based locomotion policies for mobile manipulation. We train the base
policy by applying a random wrench sequence on the robot base in simulation and
adding the noisified wrench sequence prediction to the policy observations. The
policy then learns to counteract the partially-known future disturbance. The
random wrench sequences are replaced with the wrench prediction generated with
the dynamics plans from model predictive control to enable deployment. We show
zero-shot adaptation for manipulators unseen during training. On the hardware,
we demonstrate stable locomotion of legged robots with the prediction of the
external wrench.
