SGER: Well Modeled Modular Robots for Complex Dynamic Motion

该SGER提案涉及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。相对而言，在机器人技术中捕获可重用知识的表示方面的工作很少——这是许多未来应用的核心问题。因此，SGER代表了一种潜在的变革性技术，并解决了最先进技术的重大差距，尽管存在风险，但收益非常高。我们的目标是在1年的学习中积累与抓握和操纵相关的知识。我们将扩大试点研究，建立自我激励学习技术的原型，以及操纵和多体接触关系的生成模型。这种方法依赖于在几个阶段的学习过程中发现和利用结构；从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，再到涉及堆叠和插入等任务的多体接触。该项目有三个主要的技术目标：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；并推进对人类和机器的感觉运动和认知发展的纵向过程的计算计算。

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.