论文标题
迈向协调的机器人动议：转化树木的运动政策的端到端学习
Towards Coordinated Robot Motions: End-to-End Learning of Motion Policies on Transform Trees
论文作者
论文摘要
由于机器人施加的几何约束，生成同时完成多个任务的机器人运动是具有挑战性的。在本文中，我们建议通过从人类示范中学习结构化政策来解决多任务问题。我们的结构化政策灵感来自RMPFlow，这是在不同空间上结合子任务策略的框架。该策略结构为用户提供了一个接口1）指定与任务完成直接相关的空间，以及2）为某些不需要学习的任务设计策略。我们得出一个适合多任务问题的端到端学习目标函数，强调了任务空间运动的偏差。此外，保证从学习的政策类产生的动议是稳定的。我们通过对7多种重新考虑锯豆机器人的三个机器人任务进行定性和定量评估来验证我们提出的学习框架的有效性。
Generating robot motion that fulfills multiple tasks simultaneously is challenging due to the geometric constraints imposed by the robot. In this paper, we propose to solve multi-task problems through learning structured policies from human demonstrations. Our structured policy is inspired by RMPflow, a framework for combining subtask policies on different spaces. The policy structure provides the user an interface to 1) specifying the spaces that are directly relevant to the completion of the tasks, and 2) designing policies for certain tasks that do not need to be learned. We derive an end-to-end learning objective function that is suitable for the multi-task problem, emphasizing the deviation of motions on task spaces. Furthermore, the motion generated from the learned policy class is guaranteed to be stable. We validate the effectiveness of our proposed learning framework through qualitative and quantitative evaluations on three robotic tasks on a 7-DOF Rethink Sawyer robot.
