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

在包括工厂、家庭和医院在内的广泛环境中，对电缆、布料和分散的刚性物体等物体的操作是必不可少的。虽然现代机器人在物理上有能力操纵这些物体，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。为了让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物体的移动方式。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，机器人需要有方法来推断它学过什么和没有学过什么，这样它才能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，它将获得操作特定对象的经验。机器人将需要利用这些经验来增强对物体运动方式的理解，从而获得更可靠的性能。赋予机器人从数据中学习的能力，同时意识到学习内容的不确定性，并从经验中提高他们的理解能力，将使机器人在许多工业领域得到广泛的应用。为了给机器人提供这些基本能力，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力学学习方法来计划目前被认为难以分析建模的操作任务。机器学习的最新进展已经允许从高维数据（如图像）中学习动态模型。然而，这些学习到的模型目前还不足以用于规划，因为它们没有考虑到认知的不确定性，即由于缺乏数据而产生的不确定性。不考虑认知的不确定性会导致对模型预测置信度的不可靠估计，这可能导致高度依赖状态的误差。此外，运动规划的基本进展需要与不能保证在任何地方都有效的模型进行稳健规划。因此，本项目将探索以下基本方法：1)在考虑认知不确定性的同时估计动态模型预测的置信度；2)在执行过程中使用有限的数据改进动态预测；3)原则性运动规划，使用这些预测和置信度估计来避免模型不可靠的状态空间区域。解决这个难题的关键是动力学模型不需要全局精确才能用于规划运动。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物体，如绳子、布和碎片，以完成广泛的实际任务。该项目由跨部门机器人基础研究项目支持，由工程（ENG）和计算机与信息科学与工程（CISE）联合管理和资助。该奖项反映了美国国家科学基金会的法定使命，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。

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.