EAGER: Robust Data-Driven Robotic Manipulation via Bayesian Inference and Passivity-Based Control

EAGER：通过贝叶斯推理和基于被动的控制进行稳健的数据驱动机器人操作

• 批准号: 2330794
• 负责人: Hasan Poonawala
• 金额: $ 26.22万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330794&HistoricalAwards=false
• 关键词: EAGER; Robust; Data; Driven; Robotic

Robots usually move objects by firmly holding on to them. Some tasks cannot be done this way, because the object may be delicate, or large relative to the robot's arm or hand. For example, we use firm holds when moving a closed book, but delicate finger motions when turning a page. Since the robot's "hand" may move relative to the object, the contact type between the robot, object, and environment can change during manipulation. Forces applied on the object create different motions when contact conditions are different. Conversely, different motions may lead to different contacts in the future. Planning computational methods can identify the right sequence of forces and contact conditions that could complete a task. Small errors that crop up during execution of planned motions would normally be reduced by taking corrective actions. However, these corrective actions often do not account for changing = contacts, and the errors instead are more critical due to unanticipated contacts, ultimately leading to failure on tasks. This EArly-concept Grant for Exploratory Research (EAGER) project will study techniques to create plans for robot motion that mitigate instead of amplify errors during execution of such tasks. Such manipulation tasks involving significant contact events can be found in robotic applications such as loading dishwashers, fetching hard-to-reach objects from cluttered cupboards, or moving furniture. The project team will study new data-driven methods to train robust motion controllers that are derived from Bayesian neural networks with special structure informed by robotics and control principles. To account for the contact-rich nature of the task, the network will consist of a mixture-of-experts, where each expert is either a controller or a storage function used to derive a passivity-based controller. A gating network chooses which controller to use given the input to the network. Bayesian networks will provide a distribution over motor commands given an input, allowing the motion controller to account for uncertainty. The project will proceed in three overlapping stages: The investigators will use tools from formal verification to synthesize controllers that provably locally stabilize contact-rich motion plans, and use these controllers to initialize a prior distribution for the weights of the Bayesian neural network using knowledge distillation. This initialized network will be trained from task-based rewards in an end-to-end manner using data from differentiable simulators, where the robot-object-environment system parameters are uncertain. The trained network will be tested in experiments involving a robot arm pushing a large box over step-like obstacles designed to require changes in contact conditions during manipulation. The project, if successful, will identify a controller synthesis paradigm that simultaneously overcomes the simulation-to-reality gap and the data-inefficiency plaguing purely data-driven approaches for contact-rich object manipulation. This project will also advance knowledge in scaling up computational controller synthesis, and contribute new tools for GPU-accelerated simulation of stochastic systems.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.

机器人通常通过紧紧抓住物体来移动物体。有些任务不能用这种方式完成，因为物体可能很脆弱，或者相对于机器人的手臂或手来说太大。例如，我们在移动合上的书时，用的是坚定的握法，而在翻页时，用的是细腻的手指动作。由于机器人的“手”可以相对于物体移动，因此在操作过程中，机器人、物体和环境之间的接触类型会发生变化。当接触条件不同时，施加在物体上的力产生不同的运动。相反，不同的运动可能导致未来不同的接触。规划计算方法可以确定完成任务所需的力和接触条件的正确顺序。在执行计划运动期间突然出现的小错误通常会通过采取纠正措施来减少。然而，这些纠正措施通常没有考虑到触点的变化，相反，由于未预料到的触点，错误更加严重，最终导致任务失败。这个早期概念探索性研究（EAGER）项目将研究技术，以创建机器人运动计划，减轻而不是放大执行此类任务时的错误。这种涉及重大接触事件的操作任务可以在机器人应用中找到，例如装载洗碗机，从杂乱的橱柜中取出难以触及的物体，或移动家具。项目团队将研究新的数据驱动方法来训练来自贝叶斯神经网络的鲁棒运动控制器，该网络具有机器人技术和控制原理的特殊结构。考虑到任务接触丰富的性质，网络将由专家组成，其中每个专家要么是控制器，要么是用于派生基于被动的控制器的存储函数。给定网络的输入，门控网络选择使用哪个控制器。贝叶斯网络将提供给定输入的电机命令的分布，允许运动控制器考虑不确定性。该项目将分三个重叠的阶段进行：研究人员将使用形式验证的工具来合成控制器，这些控制器可以在局部稳定富含接触的运动计划，并使用这些控制器来初始化贝叶斯神经网络权重的先验分布。这个初始化的网络将使用来自可微分模拟器的数据以端到端的方式从基于任务的奖励中进行训练，其中机器人-对象-环境系统参数是不确定的。经过训练的网络将在实验中进行测试，其中包括一个机器人手臂推动一个大盒子越过台阶状的障碍物，这种障碍物的设计要求在操作过程中改变接触条件。如果成功，该项目将确定一个控制器综合范例，同时克服模拟到现实的差距和数据效率低下困扰纯数据驱动的方法，用于富接触对象操作。该项目还将推进扩大计算控制器综合的知识，并为gpu加速的随机系统模拟提供新工具。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。