论文标题
利用人类反馈进行轮椅网球的原始优化
Utilizing Human Feedback for Primitive Optimization in Wheelchair Tennis
论文作者
论文摘要
敏捷机器人会提出一个艰难的挑战，机器人以高速移动，需要精确和低延迟的感应和控制。创建敏捷的运动在安全执行的同时完成手头的任务是敏捷机器人获得人类信任的关键要求。这就需要设计灵活的新方法并维持对世界限制的知识。在本文中，我们考虑了构建灵活和自适应控制器的问题，以挑战敏捷的移动操纵任务，即在轮椅网球机器人上击中地面行程。我们通过（1）通过（1）证明在敏捷移动操纵器设置上安全执行学习的原语，并提出（2）提出一种在线原始精炼程序，并提出一种利用人类对执行后的人类的评估反馈的在线原始精炼程序，并提出了使用概率运动原始框架（PROMP）框架学习引人注目的行为的扩展和评估工作的扩展。
Agile robotics presents a difficult challenge with robots moving at high speeds requiring precise and low-latency sensing and control. Creating agile motion that accomplishes the task at hand while being safe to execute is a key requirement for agile robots to gain human trust. This requires designing new approaches that are flexible and maintain knowledge over world constraints. In this paper, we consider the problem of building a flexible and adaptive controller for a challenging agile mobile manipulation task of hitting ground strokes on a wheelchair tennis robot. We propose and evaluate an extension to work done on learning striking behaviors using a probabilistic movement primitive (ProMP) framework by (1) demonstrating the safe execution of learned primitives on an agile mobile manipulator setup, and (2) proposing an online primitive refinement procedure that utilizes evaluative feedback from humans on the executed trajectories.
