NRI-Small: Collaborative Research: Multiple Task Learning from Unstructured Demonstrations

这个项目开发了通过将非结构化演示分解为可重用的组件技能来高效、增量地学习复杂的机器人任务的技术。贝叶斯模型将任务演示划分为更简单的组件，并识别演示中重复技能的实例。控制工程和强化学习的既定方法被利用和推广，以便除了从演示中学习之外，还可以从实践中提高技能。该项目旨在将对每个想法的现有研究统一为一个有原则的、综合的方法，共同解决所有这些问题，目标是创建一个可部署的开源系统，改变专家和新手与机器人交互的方式。一个简单的界面，允许最终用户直观地对机器人进行编程，是让机器人走出实验室，进入家庭和工作场所人类合作环境的关键一步。虽然专家经常可以为机器人编程以执行复杂的任务，但这种编程通常非常耗时，需要大量的知识。作为对此的回应，最近的研究集中在机器人从演示中学习，非专家用户可以通过示例来教机器人如何执行任务。不幸的是，这些工作大多局限于对具有明确定义的开始和结束的单个任务的人工结构化演示。相比之下，人类合作的机器人将被要求从复杂的、非专家容易制作的无结构演示中高效地、渐进地学习许多不同但往往相关的任务。

This project develops techniques for the efficient, incremental learning of complex robotic tasks by breaking unstructured demonstrations into reusable component skills. A Bayesian model segments task demonstrations into simpler components and recognizes instances of repeated skills across demonstrations. Established methods from control engineering and reinforcement learning are leveraged and extended to allow for skill improvement from practice, in addition to learning from demonstration. The project aims to unify existing research on each of these ideas into a principled, integrated approach that addresses all of these problems jointly, with the goal of creating a deployment-ready, open-source system that transforms the way experts and novices alike interact with robots.A simple interface that allows end-users to intuitively program robots is a key step to getting robots out of the laboratory and into human-cooperative settings in the home and workplace. Although it is often possible for an expert to program a robot to perform complex tasks, this programming is often very time-consuming and requires a great deal of knowledge. In response to this, much recent research is focusing on robot learning-from-demonstration, where non-expert users can teach a robot how to perform a task by example. Unfortunately, much of this work is limited to the artificially-structured demonstration of a single task with a well-defined beginning and end. By contrast, human-cooperative robots will be required to efficiently and incrementally learn many different, but often related, tasks from complex, unstructured demonstrations that are easy for non-experts to produce.