One of today's goals for industrial robot systems is to allow fast and easy provisioning for new tasks. Skill-based systems that use planning and knowledge representation have long been one possible answer to this. However, especially with contact-rich robot tasks that need careful parameter settings, such reasoning techniques can fall short if the required knowledge not adequately modeled. We show an approach that provides a combination of task-level planning and reasoning with targeted learning of skill parameters for a task at hand. Starting from a task goal formulated in PDDL, the learnable parameters in the plan are identified and an operator can choose reward functions and parameters for the learning process. A tight integration with a knowledge framework allows to form a prior for learning and the usage of multi-objective Bayesian optimization eases to balance aspects such as safety and task performance that can often affect each other. We demonstrate the efficacy and versatility of our approach by learning skill parameters for two different contact-rich tasks and show their successful execution on a real 7-DOF KUKA-iiwa.
翻译:今天工业机器人系统的目标之一是为新的任务提供快速和容易的供给。 使用规划和知识代表的基于技能的系统长期以来一直是可能的答案之一。 但是,特别是对于需要谨慎的参数设置的接触丰富的机器人任务,如果所需知识没有适当模型化,这种推理技术可能不尽如人意。 我们展示了一种结合任务规划和推理的方法,同时有针对性地学习手头任务的技能参数。 从PDDL制定的任务目标开始,确定计划中的可学习参数,操作者可以选择学习过程的奖励功能和参数。 与知识框架的紧密结合,可以形成一个学习的先入先行和使用多目标贝叶斯优化的功能,从而可以平衡诸如往往相互影响的安全性和任务性等各个方面。 我们通过学习两个不同的接触丰富任务的技能参数来展示我们方法的功效和多变性,并展示它们在实际的7DOF KUKA-iwa上的成功执行。