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

该项目通过将非结构化演示分解为可重用组件技能，开发了复杂机器人任务的高效增量学习技术。 贝叶斯模型将任务演示分割为更简单的组件，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得到了利用和扩展，除了从演示中学习之外，还可以从实践中提高技能。 该项目旨在将对这些想法的现有研究统一为一种原则性的综合方法，共同解决所有这些问题，目标是创建一个部署就绪的，一个开源系统，改变了专家和新手与机器人的互动方式。一个简单的界面，让最终用户直观地编程机器人是一个关键步骤，让机器人走出实验室，进入人类。家庭和工作场所的合作环境。 虽然专家通常可以对机器人进行编程以执行复杂的任务，但这种编程通常非常耗时，并且需要大量的知识。针对这一点，最近的许多研究都集中在机器人从演示中学习，其中非专家用户可以通过示例教机器人如何执行任务。 不幸的是，大部分的工作都局限于人工结构的演示一个单一的任务，有一个明确的开始和结束。 相比之下，人类合作机器人将需要有效地和渐进地学习许多不同的，但往往是相关的，从复杂的，非结构化的演示，很容易为非专家生产的任务。

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.