Mobile manipulation tasks remain one of the critical challenges for the widespread adoption of autonomous robots in both service and industrial scenarios. While planning approaches are good at generating feasible whole-body robot trajectories, they struggle with dynamic environments as well as the incorporation of constraints given by the task and the environment. On the other hand, dynamic motion models in the action space struggle with generating kinematically feasible trajectories for mobile manipulation actions. We propose a deep reinforcement learning approach to learn feasible dynamic motions for a mobile base while the end-effector follows a trajectory in task space generated by an arbitrary system to fulfill the task at hand. This modular formulation has several benefits: it enables us to readily transform a broad range of end-effector motions into mobile applications, it allows us to use the kinematic feasibility of the end-effector trajectory as a dense reward signal and its modular formulation allows it to generalise to unseen end-effector motions at test time. We demonstrate the capabilities of our approach on multiple mobile robot platforms with different kinematic abilities and different types of wheeled platforms in extensive simulated as well as real-world experiments.

中文翻译：

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通过深度强化学习学习移动操作的运动学可行性


移动操纵任务仍然是自主机器人在服务和工业场景中广泛采用的关键挑战之一。虽然规划方法擅长生成可行的全身机器人轨迹，但它们难以应对动态环境以及任务和环境给出的约束的结合。另一方面，动作空间中的动态运动模型很难为移动操纵动作生成运动学上可行的轨迹。我们提出了一种深度强化学习方法来学习移动底座的可行动态运动，而末端执行器遵循任意系统生成的任务空间中的轨迹来完成手头的任务。这种模块化公式有几个好处：它使我们能够轻松地将各种末端执行器运动转换为移动应用程序，它使我们能够将末端执行器轨迹的运动学可行性用作密集的奖励信号，并且其模块化公式允许它推广到测试时看不见的末端执行器运动。我们在广泛的模拟和真实实验中展示了我们的方法在具有不同运动能力和不同类型轮式平台的多个移动机器人平台上的功能。