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Overcoming Epistemic Uncertainty to Plan with Learned Dynamics Models for Robotic Manipulation

克服认知不确定性，利用学习动态模型进行机器人操作规划

基本信息

批准号：
2113401
负责人：
Dmitry Berenson
金额：
$ 60万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113401&HistoricalAwards=false
关键词：
Overcoming Epistemic Uncertainty Plan Learned

项目摘要

Manipulation of objects like cables, cloth, and scattered rigid objects is essential in a broad range of settings, including factories, homes, and hospitals. While modern robots are physically capable of manipulating these objects, they lack the algorithms necessary to understand how these objects move when being manipulated. To give robots the ability to perform a wide range of manipulation tasks necessary for maintenance, manufacturing, and cleaning tasks, this project will develop new methods that allow robots to learn how these objects move from data. Yet there will always be some uncertainty in what the robot learns, and if the robot is over-confident in its understanding, it may make many errors, or even be unable to complete the task-at-hand. Thus a robot requires methods to reason about what it has and has not learned so that it can accomplish useful tasks reliably. Finally, as the robot performs the manipulation task-at-hand it will acquire experience of manipulating that particular object. The robot will need to use that experience to enhance its understanding of how the object moves, leading to more reliable performance. Endowing robots with the ability to learn from data while being aware of the uncertainty in what they learn and improving their understanding from experience will enable a wide range of robotics applications across many sectors of industry.To provide robots with these fundamental capabilities, this project will build a much-needed bridge between the fields of dynamics learning and motion planning, enabling roboticists to take advantage of the latest dynamics learning methods to plan for manipulation tasks that are currently considered too difficult to model analytically. Recent advances in machine learning have allowed dynamics models to be learned from high-dimensional data, such as images. However, these learned models are currently insufficient for planning because they do not account for epistemic uncertainty, i.e. uncertainty due to a lack of data. Not considering epistemic uncertainty leads to unreliable estimates of a model's confidence in its prediction, which can cause highly state-dependent errors. Furthermore, fundamental advances in motion planning are required to robustly plan with models that are not guaranteed to be valid everywhere. Thus this project will explore foundational methods for 1) estimating the confidence of a dynamics model's prediction while accounting for epistemic uncertainty; 2) improving dynamics predictions using limited data during execution; and 3) principled motion planning that uses these predictions and confidence estimates to avoid areas of the state space where the model is unreliable. The key insight that enables tackling this difficult problem is that a dynamics model need not be globally-accurate to be useful for planning motion. The effectiveness of these methods will be demonstrated by integrating them into a framework that allows robots to manipulate objects such as rope, cloth, and debris for a wide range of practical tasks.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在工厂、家庭和医院等广泛的环境中，操纵电缆、布料和分散的刚性对象等对象是必不可少的。虽然现代机器人在物理上能够操纵这些物体，但它们缺乏必要的算法来理解这些物体在被操纵时如何移动。为了使机器人能够执行维护，制造和清洁任务所需的各种操作任务，该项目将开发新的方法，使机器人能够从数据中学习这些对象如何移动。然而，机器人学习的内容总是存在一些不确定性，如果机器人对自己的理解过于自信，它可能会犯很多错误，甚至无法完成手头的任务。因此，机器人需要一些方法来推理它已经学到了什么，还没有学到什么，这样它就可以可靠地完成有用的任务。最后，当机器人执行手头的操作任务时，它将获得操作该特定对象的经验。机器人将需要使用这种经验来增强其对物体如何移动的理解，从而实现更可靠的性能。赋予机器人从数据中学习的能力，同时意识到它们学习的不确定性，并从经验中提高它们的理解力，将使机器人在许多工业部门得到广泛的应用。为了使机器人具备这些基本能力，该项目将在动力学学习和运动规划领域之间建立一个急需的桥梁，使机器人专家能够利用最新的动态学习方法来规划目前被认为太难进行分析建模的操作任务。机器学习的最新进展允许从高维数据（如图像）中学习动态模型。然而，这些学习模型目前不足以进行规划，因为它们没有考虑到认知的不确定性，即由于缺乏数据而导致的不确定性。不考虑认识不确定性会导致模型对其预测的置信度的估计不可靠，这可能会导致高度依赖于状态的错误。此外，需要运动规划的根本性进步，以便使用不能保证在任何地方都有效的模型进行鲁棒规划。因此，该项目将探索基本方法：1）在考虑认识不确定性的同时估计动力学模型预测的置信度; 2）在执行过程中使用有限的数据改进动力学预测;以及3）使用这些预测和置信度估计的原则性运动规划，以避免模型不可靠的状态空间区域。能够解决这个难题的关键见解是，动力学模型不需要全局精确，就可以用于规划运动。这些方法的有效性将通过将它们集成到一个框架中来证明，该框架允许机器人操纵绳索，布料和碎片等物体，以执行广泛的实际任务。该项目得到了跨董事会机器人基础研究计划的支持，由工程局（ENG）和计算机与信息科学与工程局（CISE）共同管理和资助该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。