CAREER: Goal-Guided Self-Reflective Control Interface in Teleoperation

该项目的重点是在遥操作过程中的人机交互（HRI），为基础研究和教育活动。 该研究源于观察对象抓取和操纵使用传统的遥操作方法放置一个显着的控制负担的人类操作员，并降低任务性能。 这是因为通过机器人手对物体的间接操纵可能导致不准确的或不期望的机器人运动，而操作员可用的有限控制输入（例如，操纵杆、数据手套）使得直接运动学映射对于复杂对象操纵任务具有挑战性。 因此，人类操作员必须在精神上和身体上进行转换（例如，旋转、平移、缩放、变形）所需的机器人动作到接口处所需的输入;这些变换显著地增加了控制难度。 该项目的主要研究目标是开发一种新的目标引导的自反射控制界面（GSRCI），这将使机器人理解操作员的高层次的目标，在对象抓取操作，并符合任务约束，以减少控制难度，并确保后续操作的成功。 初级教育活动源于现有电话会议技术支持的远程学习方案的共同缺陷，即它们提供的动手学习机会有限或根本没有。 为了解决这个问题，将开发一个交互式远程学习系统（IDLS），通过学生远程控制机器人的手臂和手的对象操作和/或与其他同学的互动，使远程学生沉浸在教室环境中，从而使远程用户感觉到在教室里，并参与课堂活动。 任务建模技术以及目标导向的控制界面技术的研究工作将耦合到开发一个简单直观的远程操作为基础的远程学习系统的K-12学生。 GSRCI代表了一种变革性技术，有可能提供一种新的HRI范式，这将显着提高远程操作的功率和质量，并广泛影响与不同领域相关的应用，包括老年人和残疾人的援助，微创手术，太空和水下探索，军事侦察，核服务以及城市搜索和救援。 IDLS将促进远程学生的参与，并使他们能够成功地参与STEM活动，为弱势群体提供学习的潜力，无论他们是否有能力参加课堂设置。将创建一个认知接口，使机器人能够灵活地再现适应操作员运动输入的动作，并以自我的方式自主地调节这些动作，反射的方式来补偿任务的限制，促进后续的操作。 一个新的目标实现指标将预测计划行动的目标实现水平。 此外，一个目标导向的补救计划将调节这个动作，以实现目标，通过放松约束的操作员的运动输入使用自适应局部搜索策略。 人类和机器人基于任务的抓取行为的新模型也将被开发，这将提供一个知识库来推断人类在任务中的目标，并进行目标引导的机器人抓取规划。 为了将量化的任务约束与符号任务联系起来，考虑任务建模中的不确定性，并允许从部分观察到的数据进行任务推理，定向概率贝叶斯模型将对任务目标，对象属性，动作和任务约束之间的统计依赖性进行编码。

The focus of this project is on human-robot interactions (HRI) during teleoperation, for both the fundamental research and the educational activities. The research derives from the observation that object grasping and manipulation using conventional teleoperation approaches places a significant control burden on the human operator and reduces task performance. This is because indirect manipulation of an object through a robot hand may cause inaccurate or undesired robot motion, while the limited control inputs available to the operator (e.g., joystick, data glove) make direct kinematic mapping challenging for complex object manipulation tasks. So the human operator must mentally and physically transform (e.g., rotate, translate, scale, deform) the desired robot actions to required inputs at the interface; these transformations significantly increase control difficulty. The primary research goal of this project is to develop a novel goal-guided self-reflective control interface (GSRCI), which will enable the robot to understand the operator's high-level objective during an object-grasping operation and to conform to task constraints in order to reduce control difficulties and ensure the success of subsequent manipulation. The primary educational activity derives from the common deficiency of distance learning programs supported by existing teleconferencing technologies, namely that they offer limited or no opportunities for hands-on learning. To address this problem, an interactive distance learning system (IDLS) will be developed that immerses remote students in the classroom environment through student tele-controlling of a robot's arms and hands for object manipulation and/or interaction with other classmates, thereby enabling remote users to feel present in the classroom and engaged in class activities. The task modeling technology as well as the goal-guided control interface technology from the research work will be coupled to develop an easy and intuitive teleoperation-based distance learning system for K-12 students. The GSRCI represents transformative technology with the potential to provide a new paradigm of HRI that will significantly improve the power and quality of teleoperation and broadly impact applications related to diverse domains including assistance for the elderly and disabled, minimally invasive surgery, space and underwater explorations, military reconnaissance, nuclear servicing, and urban search and rescue. The IDLS will foster engagement for remote students and allow them to successfully participate in STEM activities, offering disadvantaged groups the potential to learn regardless of their ability to physically attend a class setting.To achieve these goals, a cognitive interface will be created that enables a robot to flexibly reproduce actions that accommodate the operator's motion inputs as well as autonomously regulate these actions in a self-reflective manner to compensate task constraints that facilitate subsequent manipulations. A novel goal-achievement indicator will predict the level of goal accomplishment for the planned action. Additionally, a goal-guided remedial planner will regulate this action to accomplish the goal by relaxing the constraint bound of the operator's motion inputs using an adaptive local search strategy. New models for human and robot task-based grasp behaviors will also be developed, which will provide a knowledge base to infer human goals in a task and to conduct goal-guided robot-grasp planning. To link quantified task constraints with symbolic tasks, account for uncertainty in task modeling, and allow task reasoning from partially observed data, a directed probabilistic Bayesian model will encode the statistical dependence among the task goal, object attributes, actions, and task constraints.