Belardinelli Anna, Wang Chao, Tanneberg Daniel, Hasler Stephan, Gienger Michael
Honda Research Institute Europe, Offenbach, Germany.
Front Robot AI. 2025 Aug 13;12:1605652. doi: 10.3389/frobt.2025.1605652. eCollection 2025.
Supportive robots that can be deployed in our homes will need to be understandable, operable, and teachable by non-expert users. This calls for an intuitive Human-Robot Interaction approach that is also safe and sustainable in the long term. Still, few studies have looked at interactive task learning in repeated, unscripted interactions within loosely supervised settings. In such cases the robot should incrementally learn from the user and consequentially expand its knowledge and abilities, a feature which presents the challenge of designing robots that interact and learn in real time. Here, we present a robotic system capable of continual learning from interaction, generalizing learned skills, and planning task execution based on the received training. We were interested in how interacting with such a system would impact the user experience and understanding. In an exploratory study, we assessed such dynamics with participants free to teach the robot simple tasks in Augmented Reality without supervision. Participants could access AR glasses spontaneously in a shared space and demonstrate physical skills in a virtual kitchen scene. A holographic robot gave feedback on its understanding and, after the demonstration, could ask questions to generalize the acquired task knowledge. The robot learned the semantic effects of the demonstrated actions and, upon request, could reproduce those on observed or novel objects through generalization. The results show that the users found the system engaging, understandable, and trustworthy, but with larger variance on the last two constructs. Participants who explored the scene more were able to expand the robot's knowledge more effectively, and those who felt they understood the robot better were also more trusting toward it. No significant variation in the user experience or their teaching behavior was found across two interactions, yet the low return rate and free-form comments hint at critical lessons for interactive learning systems.
能够部署在家庭中的辅助机器人需要让非专业用户能够理解、操作并教会它。这就需要一种直观的人机交互方法，这种方法从长远来看也是安全且可持续的。然而，很少有研究关注在宽松监督环境下的重复、无脚本交互中的交互式任务学习。在这种情况下，机器人应该从用户那里逐步学习，并相应地扩展其知识和能力，这一特性带来了设计能够实时交互和学习的机器人的挑战。在此，我们展示了一个能够从交互中持续学习、归纳所学技能并根据所接受的训练规划任务执行的机器人系统。我们感兴趣的是与这样一个系统进行交互会如何影响用户体验和理解。在一项探索性研究中，我们让参与者在无监督的情况下自由地在增强现实中教机器人简单任务，以此评估这种动态情况。参与者可以在共享空间中自发地使用增强现实眼镜，并在虚拟厨房场景中展示身体技能。一个全息机器人会对其理解给出反馈，并且在演示之后，可以提问以归纳所获得的任务知识。机器人学习了所演示动作的语义效果，并根据要求通过归纳在观察到的或新的物体上重现这些效果。结果表明，用户认为该系统有趣、易于理解且值得信赖，但在后两个方面的差异较大。更多探索场景的参与者能够更有效地扩展机器人的知识，而那些觉得自己更理解机器人的人对它也更信任。在两次交互中，未发现用户体验或他们的教学行为有显著差异，但低回报率和自由形式的评论暗示了交互式学习系统的关键教训。
