S&AS: INT: Smart And Autonomous Systems For Repair And Improvisation

机器人将对社会更有用，因为它们会响应更自然的教学模式，类似于人类的学习方式。 很难告诉机器人该做什么，更难的是告诉机器人如何处理所有可能发生的错误、意外事件和事故。该项目的重点是使机器人能够理解用户的指令，修复错误和修复损坏的过程，对象和工具，并即兴发挥，找到新的更好的方法来完成任务。研究的一个关键部分是使机器人能够从通常给儿童的教育套件和教学材料中学习。研究人员计划使用这些教学材料为机器人提供关于世界如何运作的“物理常识”，就像我们希望孩子们学习的那样。该研究将使机器人编程更容易、更便宜，并使机器人更有用，特别是支持日常生活活动的家庭和护理机器人;修理、建造和退役机器人;以及海洋和太空中的探索和工人机器人。从技术上讲，这个综合项目解决了开发机器人行为生成和学习的库方法的长期愿景和智力挑战。研究人员将建立一个大规模的长期智能物理系统，可以修复和改进所需的过程和设备。 该系统将以培训任务的课程形式从指令中学习，观看人类完成任务，指导，练习，较少指导的游戏和反思。 对拟议工作的评估将侧重于机器人如何执行教学材料中建议的活动，修复损坏的流程和设备，并创建实现新任务规范的流程和设备。将测试以下假设：1）可以在实践中建立适当的库，并随着时间的推移而增长，2）可以从大型库中获取相关经验，并将其组合以展示与人类相似的丰富行为，3）这样的库可以支持终身学习许多任务，而不是一个领域中的一个任务的单一演示。 关键思想包括使用任务和策略特定的定量模型，策略图的发展，联合收割机结合了如何完成任务的符号描述以及任务级模型，以及使用学习模拟器来支持心理实践，探索，和学习。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的评估被认为值得支持影响审查标准。

Robots will be more useful to society as they respond to more natural teaching paradigms similar to the way people learn. It is hard to tell robots what to do, and even harder to tell robots how to handle all possible errors, unanticipated events, and accidents that might happen. This project focuses on enabling robots to understand instructions from users, to fix errors and repair broken processes, objects, and tools, and to improvise and find new and better ways to do tasks. A key part of the research is to enable robots to learn from educational kits and instructional material typically given to children. The researchers plan to use these instructional materials to provide robots with "physical common sense" about how the world works, in a similar way we expect children to learn. The research will make programming robots easier and cheaper, and make robots more useful, particularly for domestic and care robots supporting everyday life activities; repair, construction, and decommissioning robots; and exploration and worker robots in the oceans and space.Technically, this integrative project addresses a longer-term vision and intellectual challenge of developing a library approach to robot behavior generation and learning. The researchers will build a large-scale long-term intelligent physical system that can repair and improvise desired processes and devices. The system will learn from instruction in the form of a curriculum of training tasks, watching humans do tasks, coaching, practice, less directed play, and reflection. Evaluation of the proposed work will focus on how well robots can perform suggested activities from the instructional material, repair broken processes and devices, and create processes and devices that achieve new task specifications. The following hypotheses will be tested: 1) appropriate libraries can be built in practice and grown over time, 2) relevant experience can be accessed from a large library and combined to exhibit rich behavior comparable to humans, and 3) such a library can support life-long learning for many tasks, rather than a single demonstration of one task in one domain. Key ideas include the use of task and strategy-specific quantitative models, the development of strategy graphs that combine both symbolic descriptions of how to do a task as well as task-level models, and the use of learned simulators to support mental practice, exploration, and learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.