Robot Learning of Assistive Manipulation Tasks by Demonstration via Head Gesture-based Interface
Robot Learning of Assistive Manipulation Tasks by Demonstration via Head Gesture-based Interface
复制标题
通过基于头部手势的界面演示机器人学习辅助操作任务
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
A. Gräser
中科院分区：
文献类型：
--
作者：
Maria Kyrarini;Quan Zheng;M. Haseeb;A. Gräser
辅助机器人操纵器有可能支持患有严重运动障碍的人的生活。它们可以帮助残疾人独立完成日常生活活动，如喝水、吃饭、操作任务和开门。一个有吸引力的解决方案是，让运动障碍用户通过演示日常生活任务来教机器人。用户通过直观的人机界面“手动”控制机器人进行演示，然后机器人学习所执行的任务。然而，运动障碍个体对机械臂的控制是一个具有挑战性的课题。本文提出了一种新的基于头部手势的机器人免提控制界面和机器人演示学习框架。基于头部手势的界面由安装在用户帽子上的摄像头组成，该摄像头记录由于头部运动而导致的观看场景的变化。采用光流进行特征提取，支持向量机进行手势分类，实现了头部手势识别。将识别的头部动作进一步映射到机器人的控制命令中，以执行物体操作任务。机器人通过生成动作序列来学习演示任务，并使用高斯混合模型方法对机器人末端执行器的演示路径进行分割。在机器人任务再现过程中，采用改进的高斯混合模型和高斯混合回归来适应环境变化。在一项涉及13名参与者的小型研究中，在现实世界的辅助机器人场景中对所提出的框架进行了评估；12名健全人，1名四肢瘫痪。提出的结果表明，所提出的框架的潜力，使严重运动障碍的个人演示日常生活任务的机器人操纵器。
Assistive robotic manipulators have the potential to support the lives of people suffering from severe motor impairments. They can support individuals with disabilities to independently perform daily living activities, such as drinking, eating, manipulation tasks, and opening doors. An attractive solution is to enable motor impaired users to teach a robot by providing demonstrations of daily living tasks. The user controls the robot ‘manually’ with an intuitive human-robot interface to provide demonstration, which is followed by the robot learning of the performed task. However, the control of robotic manipulators by motor impaired individuals is a challenging topic. In this paper, a novel head gesture-based interface for hands-free robot control and a framework for robot learning from demonstration are presented. The head gesture-based interface consists of a camera mounted on the user’s hat, which records the changes in the viewed scene due to the head motion. The head gesture recognition is performed using the optical flow for feature extraction and support vector machine for gesture classification. The recognized head gestures are further mapped into robot control commands to perform object manipulation task. The robot learns the demonstrated task by generating the sequence of actions and Gaussian Mixture Model method is used to segment the demonstrated path of the robot’s end-effector. During the robotic reproduction of the task, the modified Gaussian Mixture Model and Gaussian Mixture Regression are used to adapt to environmental changes. The proposed framework was evaluated in a real-world assistive robotic scenario in a small study involving 13 participants; 12 able-bodied and one tetraplegic. The presented results demonstrate a potential of the proposed framework to enable severe motor impaired individuals to demonstrate daily living tasks to robotic manipulators.
登记。
DOI：
10.1007/978-1-4939-7647-8_1
发表时间：
2018
期刊：
Neuromethods
影响因子：
--
作者：
Joshi,AnandA
通讯作者：
Joshi,AnandA