论文标题
使用端到端增强学习对移动操纵器的全身控制
Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning
论文作者
论文摘要
通常通过依次执行基础和操纵器运动来实现移动操作。但是，这种简化会导致效率损失，在某些情况下，工作空间大小的降低。即使已经提出了在线解决全身控制（WBC）的不同方法，但它们要么受运动模型的限制，要么不允许反应性，在线障碍。为了克服这些缺点，在这项工作中，我们建议对WBC进行端到端的增强学习（RL）方法。我们将学习的控制器与基于模拟的最新抽样方法进行了比较，并实现了更快的整体任务时间。此外，我们在挑战狭窄的走廊环境中验证了有关移动操纵器Royalpanda的学习政策。
Mobile manipulation is usually achieved by sequentially executing base and manipulator movements. This simplification, however, leads to a loss in efficiency and in some cases a reduction of workspace size. Even though different methods have been proposed to solve Whole-Body Control (WBC) online, they are either limited by a kinematic model or do not allow for reactive, online obstacle avoidance. In order to overcome these drawbacks, in this work, we propose an end-to-end Reinforcement Learning (RL) approach to WBC. We compared our learned controller against a state-of-the-art sampling-based method in simulation and achieved faster overall mission times. In addition, we validated the learned policy on our mobile manipulator RoyalPanda in challenging narrow corridor environments.
