Continuous extraction of task constraints in a robot programming by demonstration framework
Continuous extraction of task constraints in a robot programming by demonstration framework
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DOI：
10.5075/epfl-thesis-3814
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发表时间：
2007
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影响因子：
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通讯作者：
S. Calinon
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作者：
S. Calinon
机器人演示编程(RBD)，也被称为模仿学习，探索了向机器人传授新技能的用户友好的方法。RBD的最新进展确定了一些关键问题，以确保采用通用方法在不同的代理人和背景下转让技能。本文主要研究两个一般性问题，即模仿什么和如何模仿，它们分别涉及提取任务的本质特征和确定在不同情况下再现这些本质特征的方法。本工作采用的观点是，一项技能可以在轨迹水平上有效地描述，并且机器人可以通过观察该技能的多个演示来推断该技能的重要特征，假设这些重要特征在整个演示中是不变的。所提出的方法是基于在RBD应用中使用成熟的统计方法，首先使用隐马尔可夫模型(HMM)作为第一种方法，然后再结合使用高斯混合模型(GMM)和高斯混合回归(GMR)。即使这些方法被广泛应用于包括人体运动分析和机器人在内的各个研究领域，它们的使用基本上集中在手势识别上，而不是手势复制上。此外，这些模型通常是使用大型数据集离线训练的。因此，这些机器学习技术在模仿学习框架中的使用和组合是具有挑战性的，还没有得到广泛的研究。在这篇论文中，我们证明了这些方法非常适合于用户只提供少量演示的增量和主动示教场景，或者通过在他/她的身体上佩戴一组运动传感器，或者通过动觉教学来帮助机器人提高技能，即通过体现机器人并使其完成动作。然后将这些技术应用于使富士通HOAP-2和HOAP-3仿人机器人能够通过观察几种操作技能来自动提取不同的约束，并通过高斯混合回归(GMR)在各种情况下再现这些技能。本论文的贡献有三个方面：(1)它通过提出一个通用的概率框架来处理识别、概括、复制和评估问题，更具体地说，通过处理任务约束的自动提取和确定同时满足多个约束的控制器来在不同的上下文中再现技能，从而对RBD做出贡献：(2)通过提出主动的教学方法，通过使用增量脚手架过程和通过使用不同的形式产生演示来使人类教师进入机器人学习的循环，从而对人-机器人交互(HRI)做出贡献；(3)通过展示交流手势和操作技能的学习和复制的框架的各种真实世界应用，它最终为机器人学和HRI做出了贡献。通过在协作实验中与其他学习方法的联合使用，也展示了所提出的框架的一般性。
Robot Programming by Demonstration (RbD), also referred to as Learning by Imitation, explores user-friendly means of teaching a robot new skills. Recent advances in RbD have identified a number of key-issues for ensuring a generic approach to the transfer of skills across various agents and contexts. This thesis focuses on the two generic questions of what-to-imitate and how-to-imitate, which are respectively concerned with the problem of extracting the essential features of a task and determining a way to reproduce these essential features in different situations. The perspective adopted in this work is that a skill can be described efficiently at a trajectory level and that the robot may infer what are the important characteristics of the skill by observing multiple demonstrations of it, assuming that the important characteristics are invariant across the demonstrations. The proposed approach is based on the use of well-established statistical methods in a RbD application, by using Hidden Markov Model (HMM) as a first approach and then moving on to the joint use of Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR). Even if these methods were applied extensively in various fields of research including human motion analysis and robotics, their use essentially focused on gesture recognition rather than on gesture reproduction. Moreover, the models were usually trained offline using large datasets. Thus, the use and combination of these machine learning techniques in a Learning by Imitation framework is challenging and has not been extensively studied yet. In this thesis, we show that these methods are well suited for incremental and active teaching scenarios where the user only provides a small number of demonstrations, either by wearing a set of motions sensors attached to his/her body, or by helping the robot refine its skill by kinesthetic teaching, that is, by embodying the robot and putting it through the motion. These techniques are then applied for enabling a Fujitsu HOAP-2 and HOAP-3 humanoid robot to extract automatically different constraints by observing several manipulation skills and to reproduce these skills in various situations through Gaussian Mixture Regression (GMR). The contributions of this thesis are threefold: (1) it contributes to RbD by proposing a generic probabilistic framework to deal with recognition, generalization, reproduction and evaluation issues, and more specifically to deal with the automatic extraction of task constraints and with the determination of a controller satisfying several constraints simultaneously to reproduce the skill in a different context; (2) it contributes to Human-Robot Interaction (HRI) by proposing active teaching methods that puts the human teacher "in the loop" of the robot's learning by using an incremental scaffolding process and by using different modalities to produce the demonstrations; (3) it finally contributes to robotics and HRI through various real-world applications of the framework showing learning and reproduction of communicative gestures and manipulation skills. The generality of the proposed framework is also demonstrated through its joint use with other learning approaches in collaborative experiments.