  This work presents HiLMa-Res, a hierarchical framework leveraging
reinforcement learning to tackle manipulation tasks while performing continuous
locomotion using quadrupedal robots. Unlike most previous efforts that focus on
solving a specific task, HiLMa-Res is designed to be general for various
loco-manipulation tasks that require quadrupedal robots to maintain sustained
mobility. The novel design of this framework tackles the challenges of
integrating continuous locomotion control and manipulation using legs. It
develops an operational space locomotion controller that can track arbitrary
robot end-effector (toe) trajectories while walking at different velocities.
This controller is designed to be general to different downstream tasks, and
therefore, can be utilized in high-level manipulation planning policy to
address specific tasks. To demonstrate the versatility of this framework, we
utilize HiLMa-Res to tackle several challenging loco-manipulation tasks using a
quadrupedal robot in the real world. These tasks span from leveraging
state-based policy to vision-based policy, from training purely from the
simulation data to learning from real-world data. In these tasks, HiLMa-Res
shows better performance than other methods.
