Learning Reactive Motion Policies in Multiple Task Spaces from Human Demonstrations
Learning Reactive Motion Policies in Multiple Task Spaces from Human Demonstrations
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发表时间：
2019
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通讯作者：
M. A. Rana;Anqi Li;H. Ravichandar;Mustafa Mukadam;S. Chernova;D. Fox;Byron Boots;Nathan D. Ratliff
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作者：
M. A. Rana;Anqi Li;H. Ravichandar;Mustafa Mukadam;S. Chernova;D. Fox;Byron Boots;Nathan D. Ratliff
：复杂的操作任务往往需要机器人不同部分的不平凡和协调的运动。在这项工作中，我们解决了与学习和复制执行此类复杂任务所需的技能相关的挑战。具体地说，我们将一个任务分解为多个子任务，并通过从演示中学习稳定的策略来学习重现这些子任务。通过利用RMPflow框架进行运动生成，我们的方法fi在配置fi配置空间中提供稳定的全局策略，使各种学习子任务能够同时执行。由此得到的全局策略是学习策略的加权组合，使得运动在机器人的运动学和环境约束下是协调和可行的。我们论证了在Franka Emika机器人执行多约束操作任务的情况下，所提出方法的必要性和fi的准确性。
: Complex manipulation tasks often require non-trivial and coordinated movements of different parts of a robot. In this work, we address the challenges associated with learning and reproducing the skills required to execute such complex tasks. Specifically, we decompose a task into multiple subtasks and learn to reproduce the subtasks by learning stable policies from demonstrations. By leveraging the RMPflow framework for motion generation, our approach finds a stable global policy in the configuration space that enables simultaneous execution of various learned subtasks. The resulting global policy is a weighted combination of the learned policies such that the motions are coordinated and feasible under the robot’s kinematic and environmental constraints. We demonstrate the necessity and efficacy of the proposed approach in the context of multiple constrained manipulation tasks performed by a Franka Emika robot.