EAGER: Machines that Learn and Teach Seamlessly

这项拟议的工作旨在开发一种计算方法，可以用来从人类那里学习一项技能，并通过同样的媒介，转身向其他不太熟练的人类传授它所学到的东西。学习和教学将通过包含与所需技能相关的知识的代理来完成。这样的代理可以被称为学习和教学代理(LATA)。这项工作计划通过两种顺序的方法来实现这一点：1)观察学习(仅由代理人)，以及2)强迫反馈学习和教学。观察性学习将被用来通过观察人类在模拟器上执行期望的任务或展示期望的技能来建立一个最不熟练的LATA代理。该代理将被称为基准代理。然后，将通过力反馈学习来增强这一基线代理，当LATA代理在执行任务时出错时，人类将通过实时提供纠正的反力来指导系统。要学习/传授的技能将需要使用触觉设备，如操纵杆或方向盘作为主要界面。它将学习训练者用来胜任执行任务的动作，并使用相同的触觉设备来指导和/或评估学习相同技能的不太熟练的人类受训者。计划中的方法以使用神经进化技术为中心。神经进化已经成功地用于解决高度复杂的问题，如杆子平衡、驾驶员的异常行为，以及进化视频游戏中的机器人，逐步提高它们的性能。这项拟议的工作将根据需要修改神经进化技术的基本概念，并将所产生的系统应用于观察性学习和力反馈改进。试验台领域将是一台起重机，它将箱子或集装箱从船上卸下来，并将它们放在一些其他运输工具上，如有轨电车或卡车。将使用计算机模拟来教拉塔特工如何做到这一点。指导员越来越难找到，特别是对于需要特殊技能的专业领域。从实践的角度来看，这项研究将为参与培训的组织提供新的工具来教育他们的选民。这项技术的具体受益者将包括培训学生执行需要复杂运动技能的任务的组织，如驾驶汽车、驾驶飞机或操作起重机。产生的代理还可以用于培训远程操作机器人、起重机、无人驾驶飞行器和其他此类设备的操作员。手术培训是这种方法的另一个潜在应用，因为有了适当的触觉设备。另一个有趣的应用可能是训练残疾人的基本运动技能。

This proposed work seeks to develop a computational approach that can be used to learn a skill from humans and, through the same medium, turn around and teach other less proficient humans what it learned. The learning and teaching will be done through agents that contain the knowledge relevant to the desired skills. Such an agent can be referred to as a learning and teaching agent (LATA). The work plans to accomplish this through two sequential approaches: 1) observational learning (by the agent only), and 2) force feedback learning and teaching. Observational learning will be used to build a minimally proficient LATA agent by observing a human perform the desired task or display the desired skill on a simulator. This agent will be called the baseline agent. This baseline agent will then be enhanced through force feedback learning, where a human will coach the system by providing corrective counter force in real time when the LATA agent errs in its performance of the task. The skills to be learned/taught will require the use of a haptic device such as a joystick or steering wheel as the primary interface. It will learn the actions that the trainer employs to execute the task competently, and use the same haptic device to coach and/or evaluate a less proficient human trainee in learning the same skill. The planned approach centers on using neuroevolutionary techniques. Neuroevolution has been successfully used to address highly complex problems such as pole balancing, abnormal behavior in drivers, and to evolve bots in video games that gradually improve their performance. The proposed work will modify the basic concept of neuroevolutionary techniques as necessary, and apply the resulting system to observational learning as well as force feedback refinement. The testbed domain will be a crane that off-loads boxes or containers from a ship and places them in some other conveyance such as a railroad car or truck. A computer simulation will be used for teaching the LATA agents how to do this. Instructors are increasingly difficult to find, especially for specialty areas that require special skills. From a practical standpoint, this research would give organizations involved in training new tools to teach their constituents. Specific beneficiaries of this technology would include organizations that train students to perform tasks requiring complex motor skills such as driving a car, flying an airplane or operating a crane. The resulting agents could also be used to train operators of tele-operated robots, cranes, unmanned aerial vehicles and other such devices. Surgery training is another potential application of this approach given the appropriate haptic devices. Another interesting application could be for training disabled people basic motor skills.