Metric-based imitation learning in humans and robots

人类和机器人基于度量的模仿学习

• 批准号: 449154371
• 负责人: Professor Jan Reinhard Peters, Ph.D.
• 金额: --
• 依托单位: Fachgebiet Intelligente Autonome Systeme
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/449154371?language=en
• 关键词: Metric; based; imitation; learning; humans

Learning by imitation is a versatile and rapid mechanisms to transfer motor skills from one intelligent agent (humans, animals and robots) to another – which can be observed in Nature as well as applied in form of “programming by demonstration” in artificial systems. Infants, for example, react to the perception of facial gestures by producing similar behavior. In robotics, computational approaches to motor learning by imitation are long considered the most promising path to success. The development of autonomous robotic systems that can learn from human demonstrations to imitate a desired behavior - rather than being manually programmed - has huge technological potential with applications in manufacturing, elderly care and the service industry and is currently at the focus of robotics research. Unlike trial-and-error-based learning methods such as reinforcement learning, imitation allows rapid learning. Approaches to imitation learning have delivered huge success ranging from helicopter acrobatics, high-speed arm skills, haptic control, gestures, manipulation to legged locomotion. The machine learning algorithms that make imitation learning possible are well studied.Surprisingly, despite all of these impressive successes in the acquisition of new motor skills in robotic systems by imitation learning, fundamental scientific research questions in imitation learning of central importance have remained open for decades. Among such core questions is the one of the correspondence problem: how can one agent (the learner or imitator) produce a similar behavior - in some aspect - with behavior it perceives in another agent (the expert or demonstrator) given that the two agents have different kinematic and dynamics (body morphology, degrees of freedom, constraints, joints and actuators, torque limits), or in other words, cover different state spaces?Thus, the goal of this project is to use a metric understanding of embodiment to improve robotic motor skills through expert observations. We aim to shed light into important fundamental research questions on the (i) role of learner’s embodiment in statistical imitation learning, (ii) how the correspondence problem can be formalized properly, (iii) how can the behavior transferability vs task complexity dilemma be resolved and (iv) how to develop new statistical deep imitation learning algorithms based on these insights.

通过模仿学习是一种将运动技能从一个智能代理（人类，动物和机器人）转移到另一个智能代理的通用和快速机制-这可以在自然界中观察到，也可以在人工系统中以“演示编程”的形式应用。例如，婴儿对面部表情的感知做出反应，产生类似的行为。在机器人技术中，通过模仿进行运动学习的计算方法长期以来被认为是最有前途的成功之路。自主机器人系统的开发可以从人类演示中学习模仿所需的行为-而不是手动编程-具有巨大的技术潜力，可应用于制造业，老年人护理和服务业，目前是机器人研究的重点。与强化学习等基于试错的学习方法不同，模仿允许快速学习。模仿学习的方法已经取得了巨大的成功，从直升机杂技，高速手臂技能，触觉控制，手势，操纵到腿部运动。令人惊讶的是，尽管机器人系统通过模仿学习获得新的运动技能取得了令人印象深刻的成功，但模仿学习中至关重要的基础科学研究问题几十年来一直没有解决。在这些核心问题中，有一个是对应问题：一个特工怎么可能（学习者或模仿者）产生类似的行为-在某些方面-与它在另一个代理中感知到的行为（专家或演示者）鉴于两个代理具有不同的运动学和动力学（身体形态，自由度，约束，关节和执行器，扭矩限制），或者换句话说，覆盖不同的状态空间？因此，本项目的目标是使用对实施例的度量理解，通过专家观察来提高机器人运动技能。我们的目标是揭示重要的基础研究问题：（i）学习者的体现在统计模仿学习中的作用，（ii）如何正确地形式化对应问题，（iii）如何解决行为可转移性与任务复杂性的困境，以及（iv）如何基于这些见解开发新的统计深度模仿学习算法。