  This work developed a meta-learning approach that adapts the control policy
on the fly to different changing conditions for robust locomotion. The proposed
method constantly updates the interaction model, samples feasible sequences of
actions of estimated the state-action trajectories, and then applies the
optimal actions to maximize the reward. To achieve online model adaptation, our
proposed method learns different latent vectors of each training condition,
which are selected online given the newly collected data. Our work designs
appropriate state space and reward functions, and optimizes feasible actions in
an MPC fashion which are then sampled directly in the joint space considering
constraints, hence requiring no prior design of specific walking gaits. We
further demonstrate the robot's capability of detecting unexpected changes
during interaction and adapting control policies quickly. The extensive
validation on the SpotMicro robot in a physics simulation shows adaptive and
robust locomotion skills under varying ground friction, external pushes, and
different robot models including hardware faults and changes.
