EAGER: Functional Imitation of Observed Tasks by Co-Robots

辅助和服务机器人已经取得了重大进展，并有可能在许多领域带来变革，包括医疗保健、老龄化、残疾管理和危险环境中的工作。然而，重要的是，这些机器人可以“编程”，并由大部分未经训练的用户使用，包括护理人员或老年人。该项目旨在开发一种新方法，通过观察人类进行的演示，使各种设计的机器人能够执行辅助和支持任务。与模仿人类的动作不同，这种方法要求机器人模仿人类的动作，而是利用这些演示来“推断”任务的重要方面，并将其转化为机器人在不同情况和环境下可以执行的策略。提出的方法不是将模仿视为对观察到的动作的复制，而是学习复制演示的功能。为此，它使用观察到的环境动力学的学习模型将观察结果转换为分层马尔可夫任务模型。该概率任务模型随后被映射到机器人行为能力的分层半马尔可夫决策模型，该模型使用自适应相似性函数表示两个模型中属性之间的对应关系以及特定属性对成功任务执行的重要性。在模仿过程中使用强化学习和用户的定性反馈来调整成本函数，使系统能够改进和个性化其模仿能力。本项目开发了一个概念验证系统，并在一个轮式移动机械手上对其进行了评估。

Assistive and service robots have made significant strides and have the potential to be transformative in many fields, including health care, aging, disability management, and work in dangerous environments. For this, however, it is important that these robots can be "programmed" and used by largely untrained users, including caregivers or elderly persons. This project aims to develop a novel approach to allow robots of widely varying designs to perform assistive and supportive tasks by observing demonstrations performed by a human. Rather than copying movement, which would require that the robot resembles the human, the proposed approach uses these demonstrations to "infer" the important aspects of the task and translate them into a strategy that can be executed by the robot and in varying situations and settings.The proposed approach treats imitation not as copying of observed movements but rather as learning to replicate the function of the demonstration. For this, it transforms observations into a hierarchical Markov task model using learned models of observed environmental dynamics. This probabilistic task model is then mapped onto a hierarchical Semi-Markov Decision model of the robot's behavioral capabilities using an adaptive similarity function that represents the correspondence between attributes in the two models as well as the importance of particular attributes for successful task performance. The cost function is adapted during imitation using Reinforcement Learning and qualitative feedback from the user, allowing the system to improve and personalize its imitation capabilities. This project develops a proof-of-concept system and evaluates it on a wheeled mobile manipulator in the context of common household tasks.