论文标题
学习以对象为中心的操纵技巧的工业任务的学习和测序
Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks
论文作者
论文摘要
使机器人能够快速学习操纵技巧是一个重要但充满挑战的问题。这样的操作技巧应该是灵活的，例如，能够适应当前的工作区配置。此外，为了完成复杂的操纵任务，机器人应该能够对多种技能进行测序并使它们适应不断变化的情况。在这项工作中，我们提出了一种快速的机器人技能序列算法，其中技能是由以对象为中心的隐藏半马尔科夫模型编码的。学习的技能模型可以编码多模式（时间和空间）轨迹分布。这种方法大大减少了手动建模工作，同时确保了高度的灵活性和学习技能的可重复性。鉴于任务目标和一系列通用技能，我们的框架计算技能实例之间的平稳过渡。为了计算任务空间中相应的最佳终端效果轨迹，我们依靠Riemannian最佳控制器。我们在工业组装任务的7 DOF机器人部门上演示了这种方法。
Enabling robots to quickly learn manipulation skills is an important, yet challenging problem. Such manipulation skills should be flexible, e.g., be able adapt to the current workspace configuration. Furthermore, to accomplish complex manipulation tasks, robots should be able to sequence several skills and adapt them to changing situations. In this work, we propose a rapid robot skill-sequencing algorithm, where the skills are encoded by object-centric hidden semi-Markov models. The learned skill models can encode multimodal (temporal and spatial) trajectory distributions. This approach significantly reduces manual modeling efforts, while ensuring a high degree of flexibility and re-usability of learned skills. Given a task goal and a set of generic skills, our framework computes smooth transitions between skill instances. To compute the corresponding optimal end-effector trajectory in task space we rely on Riemannian optimal controller. We demonstrate this approach on a 7 DoF robot arm for industrial assembly tasks.
