SGER: Well Modeled Modular Robots for Complex Dynamic Motion

这个SGER建议关注与非结构化环境交互的机器人的技能和控制知识的积累和表示。在机器人技术中，捕捉可重用知识的表示方法相对较少-这是许多未来应用的核心问题。因此，该SGER代表了一种潜在的变革性技术，并解决了最先进技术中的重大差距，尽管存在风险，但回报非常高。我们的目标是我们为期1年的学习技术，积累知识的掌握和操作。我们将扩大试点研究，并建立原型的自我激励的学习技术和生成模型的操纵和多体接触关系。这种方法依赖于学习在几个阶段的学习过程中发现和利用结构;从关于机器人本身的感觉和运动知识，到机器人与外部物体之间的可控关系，再到堆叠和插入等任务中涉及的多体接触。该项目有三个主要技术目标：推进机器人操作和知识表示的最新技术;将机器学习方法扩展到内在动机，累积和分层学习;以及推进人类和机器的感觉运动和认知发展纵向过程的计算帐户。

This SGER proposal concerns the accumulation and representation of skills and control knowledge by robots that interact with unstructured environments. There has been comparatively little work on representations that capture re-useable knowledge in robotics---an issue that lies at the heart of many future applications. Thus, this SGER represents a potentially transformative technology and addresses significant gaps in the state-of-the-art for which the payoff, despite the risk, is extremely high. We aim our 1 year study on learning techniques that accumulate knowledge related to grasping and manipulation. We shall extend pilot studies and build prototypes for self-motivated learning techniques and generative models for manipulation and multi-body contact relationships. The approach relies on learning to discover and exploit structure over the course of several staged learning episodes; from sensory and motor knowledge concerning the robot itself, to controllable relationships between the robot and external bodies, to multi-body contacts involved in tasks like stacking and insertion. The project has three principal technological goals: to advance the state-of-the-art of robotic manipulation and knowledge representation; to extend machine learning methods toward intrinsically motivated, cumulative, and hierarchical learning; and to advance computational accounts of the longitudinal processes of sensorimotor and cognitive development in humans and machines.