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.

翻译：本文提出HiLMa-Res，一种利用强化学习的分层框架，旨在解决四足机器人在执行连续运动的同时完成操作任务的问题。与以往多数专注于解决特定任务的研究不同，HiLMa-Res被设计为通用框架，适用于各种需要四足机器人保持持续移动能力的运动-操作任务。该框架的新颖设计解决了利用腿部集成连续运动控制与操作所面临的挑战。它开发了一个操作空间运动控制器，该控制器能够在以不同速度行走时跟踪任意的机器人末端执行器（足尖）轨迹。此控制器被设计为可泛化至不同的下游任务，因此可用于高层操作规划策略以解决具体任务。为展示该框架的通用性，我们在现实世界中利用HiLMa-Res使四足机器人完成了多项具有挑战性的运动-操作任务。这些任务涵盖从基于状态策略到基于视觉策略，从纯仿真数据训练到真实世界数据学习。在这些任务中，HiLMa-Res均表现出优于其他方法的性能。