Performing bimanual tasks with dual robotic setups can drastically increase the impact on industrial and daily life applications. However, performing a bimanual task brings many challenges, like synchronization and coordination of the single-arm policies. This article proposes the Safe, Interactive Movement Primitives Learning (SIMPLe) algorithm, to teach and correct single or dual arm impedance policies directly from human kinesthetic demonstrations. Moreover, it proposes a novel graph encoding of the policy based on Gaussian Process Regression (GPR) where the single-arm motion is guaranteed to converge close to the trajectory and then towards the demonstrated goal. Regulation of the robot stiffness according to the epistemic uncertainty of the policy allows for easily reshaping the motion with human feedback and/or adapting to external perturbations. We tested the SIMPLe algorithm on a real dual-arm setup where the teacher gave separate single-arm demonstrations and then successfully synchronized them only using kinesthetic feedback or where the original bimanual demonstration was locally reshaped to pick a box at a different height.

翻译：使用双机械臂系统执行双臂任务可显著提升其在工业及日常生活应用中的影响力。然而，双臂任务的执行面临诸多挑战，例如单臂策略的同步与协调问题。本文提出安全交互式运动基元学习（SIMPLe）算法，通过人体运动示教直接教授并修正单臂或双臂阻抗策略。此外，本文基于高斯过程回归（GPR）提出一种新颖的策略图编码方法，可保证单臂运动收敛至轨迹附近并最终向示教目标逼近。根据策略的认知不确定性调节机器人刚度的机制，使得通过人类反馈重塑运动或适应外部扰动变得简便易行。我们在真实双臂平台上对SIMPLe算法进行了验证：实验中，教师分别提供单臂示教，随后仅通过运动示教反馈成功实现双臂同步；或对原始双臂示教进行局部重塑，以在不同高度抓取箱子。