Learning from demonstration (LfD) has the potential to greatly increase the applicability of robotic manipulators in modern industrial applications. Recent progress in LfD methods have put more emphasis in learning robustness than in guiding the demonstration itself in order to improve robustness. The latter is particularly important to consider when the target system reproducing the motion is structurally different to the demonstration system, as some demonstrated motions may not be reproducible. In light of this, this paper introduces a new guided learning from demonstration paradigm where an interactive graphical user interface (GUI) guides the user during demonstration, preventing them from demonstrating non-reproducible motions. The key aspect of our approach is determining the space of reproducible motions based on a motion planning framework which finds regions in the task space where trajectories are guaranteed to be of bounded length. We evaluate our method on two different setups with a six-degree-of-freedom (DOF) UR5 as the target system. First our method is validated using a seven-DOF Sawyer as the demonstration system. Then an extensive user study is carried out where several participants are asked to demonstrate, with and without guidance, a mock weld task using a hand held tool tracked by a VICON system. With guidance users were able to always carry out the task successfully in comparison to only 44% of the time without guidance.

翻译：从演示中学习（LfD）有望显著提升工业应用中机械臂的实用性。近期LfD方法的进展更多侧重于学习鲁棒性，而非通过引导演示本身来提升鲁棒性。当复现动作的目标系统与演示系统存在结构差异时，后者尤为重要——因为某些演示动作可能无法被复现。鉴于此，本文提出一种新的引导式演示学习范式：通过交互式图形用户界面（GUI）在演示过程中引导操作者，避免其演示不可复现的动作。该方法的核心在于：基于运动规划框架确定可复现动作的空间，该框架能定位任务空间中轨迹长度有界保证的区域。我们以六自由度（DOF）UR5作为目标系统，在两种不同实验配置下评估方法性能。首先使用七自由度Sawyer作为演示系统验证方法，随后开展大规模用户研究：要求多名参与者分别在有/无引导条件下，使用VICON系统追踪的手持工具完成模拟焊接任务。实验表明，有引导条件下参与者总能成功完成任务，而无引导时成功率仅为44%。