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人工手臂的直接预测协同控制

Direct Predictive Collaborative Control of a Prosthetic Arm
课程网址: http://videolectures.net/rldm2015_pilarski_prosthetic_arm/  
主讲教师: Patrick M. Pilarski
开课单位: 阿尔伯塔大学
开课时间: 2015-07-28
课程语种: 英语
中文简介:
我们开发了一个在线学习系统,用于辅助设备的协同控制。协同控制是一个复杂的环境,需要人类用户和学习系统(自动化)合作,以实现用户的目标。在许多控制域中,用户可以使用的可控功能的数量超过了用户在给定时刻可以处理的功能。通过控制那些无人参与的功能,这些域可以从自动化帮助用户中获益。用户决策和自动化决策之间的这种交互究竟应该如何发生还不清楚,也不清楚自动化在多大程度上是有益的或是需要的。我们应该期待这样的答案在不同的领域和可能的时刻有所不同。人们感兴趣的一个领域是截肢者对电动假肢的控制。上肢截肢者可以提供给假肢装置的输入量极为有限,通常一次只能控制一个关节,并且能够在关节之间切换。现代假体的控制往往被使用者认为是费力和非直观的。为了解决这些困难,我们开发了一个称为直接预测协同控制(DPCC)的协作控制框架,它使用一种称为一般值函数的强化学习技术来对用户行为进行时间预测。这些预测直接映射到无人值守执行器的控制,以产生运动协同效应。我们评估了在人类控制动力多关节手臂过程中的DPCC。我们证明了DPCC提高了用户执行协调移动任务的能力。另外,我们还证明了这种方法不需要特定的训练环境,只需要从用户的行为中学习。据我们所知,这也是首次将新的真在线TD(lambda)算法与一般值函数相结合用于在线控制的演示。
课程简介: We have developed an online learning system for the collaborative control of an assistive device. Collaborative control is a complex setting requiring a human user and a learning system (automation) to co-operate towards achieving the user’s goals. There are many control domains where the number of controllable functions available to a user surpass what a user can attend to at a given moment. Such domains may benefit from having automation assist the user by controlling those unattended functions. How exactly this interaction between user decision making and automated decision making should occur is not clear, nor is it clear to what degree automation is beneficial or desired. We should expect such answers to vary from domain to domain and possibly from moment to moment. One domain of interest is the control of powered prosthetic arms by amputees. Upper-limb amputees are extremely limited in the number of inputs they can provide to a prosthetic device and typically control only one joint at a time with the ability to toggle between joints. Control of modern prostheses is often considered by users to be laborious and non-intuitive. To address these difficulties, we have developed a collaborative control framework called Direct Predictive Collaborative Control (DPCC), which uses a reinforcement learning technique known as general value functions to make temporal predictions about user behavior. These predictions are directly mapped to the control of unattended actuators to produce movement synergies. We evaluate DPCC during the human control of a powered multi-joint arm. We show that DPCC improves a user’s ability to perform coordinated movement tasks. Additionally, we demonstrate that this method can be used without the need for a specific training environment, learning only from user’s behavior. To our knowledge this is also the first demonstration of the combined use of the new True Online TD(lambda) algorithm with general value functions for online control.
关 键 词: 协作控制; 可控制; 自动化
课程来源: 视频讲座网
数据采集: 2020-12-21:yxd
最后编审: 2020-12-21:yxd
阅读次数: 33