S&AS: FND: Reflective Learning of Stochastic Physical Models for Robust Manipulation

为了让机器人在日常生活环境中发挥作用，从灵活的制造和仓库领域到家庭，它们需要自主地抓取和操纵各种可能未知的物体。目前，自主机器人实际上无法在高度受控的环境之外工作，其中提供了每个对象的精确模型。这种限制部分是由于缺乏鲁棒的算法来抓取和操纵具有未知几何或机械特性的物体。拟议的项目将对强大的机器人操作进行基础研究，使自主机器人能够在较长时间内与各种日常物理对象进行有效交互。我们的目标是自主机器人操作器，以有效地学习经验，对象如何可能物理相互作用，并与机械臂。下一步是利用这种经验，以便执行强大的操作任务。有许多示例性的机器人任务可以从所提出的改进中受益，并将构成该项目实验过程的基础。它们包括将物体推到所需的姿势，重新配置物体以简化它们的拾取和工具的处理。该项目开发了三个关键组成部分：（1）通过观察机器人操纵物体时物体如何移动来学习未知物体的惯性，弹性和摩擦特性的算法。该项目将研究用于黑盒系统识别的新型贝叶斯优化技术，以学习对象的概率模型。(2)一种物理上真实的模拟器，其可以提供给定由第一组件学习的物理参数的对象运动的随机模型。这将通过利用在线非参数学习方法来实现，以加快不确定性下的物理现实模拟。(3)一种鲁棒的规划算法，它利用模拟器来找到最佳动作，以应用于给定学习的随机模型的对象。我们的目标是收敛到越来越强大的解决方案，计算时间的增加和机器人获得更多的经验与对象的环境。为了加强该项目更广泛的影响，PI将向研究界提供其解决方案的实现，作为开源软件包。这将与教育材料的生成相结合，旨在吸引本科生在学习STEM的早期。PI还将组织学术会议，将基础领域的研究人员、机器人专家和行业代表聚集在一起。

In order for robots to function in everyday life environments, from flexible manufacturing and warehouse domains to households, they need to autonomously grasp and manipulate a wide variety of potentially unknown objects. Currently, autonomous robots are practically unable to work outside highly-controlled environments wherein an accurate model of every object is provided. This limitation is partially due to the lack of robust algorithms for grasping and manipulating objects with unknown geometric or mechanical properties. The proposed project will perform fundamental research into robust robotic manipulation in a way that will enable autonomous robots to interact efficiently with a large variety of everyday physical objects for extended periods of time. The objective is for autonomous robotic manipulators to effectively learn from experience how objects may physically interact with each other and with the robotic arm. The next step is to utilize this experience so as to perform robust manipulation tasks. There are many exemplary robotic tasks that can be benefited from the proposed improvements and which will form the basis of the project's experimentation process. They include the pushing of objects to desired poses, reconfiguration of objects to simplify their picking and the handling of tools. The project develops three key components: (1) An algorithm for learning inertial, elastic, and friction properties of an unknown object by observing how the object moves when manipulated by a robot. The project will research novel Bayesian optimization techniques for black-box system identification in order to learn probabilistic models of objects. (2) A physically realistic simulator that can provide a stochastic model of an object's motion given the physical parameters learned by the first component. This will be achieved by utilizing online non-parametric learning methods for speeding up physically realistic simulations under uncertainty. (3), A robust planning algorithm that utilizes the simulator for finding optimal actions to apply on the object given the learned stochastic model. The objective is to converge to increasingly robust solutions as computation time increases and the robot acquires increased experience with objects in an environment. To strengthen the project's broader impact, the PIs will provide implementations of their solutions to the research community as open-source software packages. This will be coupled with the generation of educational material, which will aim to attract undergraduate students early in their studies to STEM. The PIs will also aim to organize academic meetings that will bring together researchers from foundational domains, robotics experts and industry representatives.