This work investigates how the intricate task of grasping may be learned from humans based on demonstrations and corrections. Due to the complexity of the task, these demonstrations are often slow and even slightly flawed, particularly at moments when multiple aspects (i.e., end-effector movement, orientation, and gripper width) have to be demonstrated at once. Rather than training a person to provide better demonstrations, non-expert users are provided with the ability to interactively modify the dynamics of their initial demonstration through teleoperated corrective feedback. This in turn allows them to teach motions outside of their own physical capabilities. In the end, the goal is to obtain a faster but reliable execution of the task. The presented framework learns the desired movement dynamics based on the current Cartesian Position with Gaussian Processes (GP), resulting in a reactive, time-invariant policy. Using GPs also allows online interactive corrections and active disturbance rejection through epistemic uncertainty minimization. The experimental evaluation of the framework is carried out on a Franka-Emika Panda.

翻译：由于任务的复杂性,这些演示往往缓慢,甚至有轻微的缺陷,特别是在必须同时展示多个方面(即终端效应运动、定向和抓抓宽度)的时候。非专家用户不是要培训一个人提供更好的演示,而是要通过远程操作纠正反馈,让非专家用户有能力通过互动方式改变其初步演示的动态。这反过来又使他们能够在自身体能之外进行教学。最后,目标是更快而可靠地执行这项任务。提出的框架根据目前与高斯进程(GP)的卡尔蒂斯人立场,了解理想的流动动态,从而形成一种反应性、时间变化性的政策。使用GP还允许在线互动纠正,并通过尽量减少突变不确定性来积极抵制扰动。框架的实验性评估是在弗朗卡-埃米卡潘达进行的。