NRI: INT: COLLAB: Integrated Modeling and Learning for Robust Grasping and Dexterous Manipulation with Adaptive Hands

机器人需要有效地与仓库、工厂以及家庭和办公室中出现的各种对象进行交互。这需要通过低成本的机器人和低复杂性的解决方案对日常对象进行健壮的抓取和灵活的操作。传统上，机器人使用僵硬的手和分析模型来完成这类任务，即使存在微小的错误也经常失败。顺从的新手承诺提高性能，同时将复杂性降至最低，并增加健壮性。然而，它们本身就很难感知和建模。这个项目结合了不同机器人领域的想法来解决这个限制。它利用了机器学习的进步，并建立在机器人建模的强大传统之上。其目标是提供自适应、顺应的机器人，在存在多个未知接触点和手中滑动或滚动物体的情况下更好地抓取对象。新的或改进的机械手设计、改进的控制算法和软件以及相应的数据集的公开发布将加强更广泛的影响。此外，学术传播将伴随着对研究生和高中生的教育推广。为了实现上述目标，第一步将是定义适用于适应能力强、顺从的手的新的混合模式。这将通过改进分析解决方案并将其扩展到允许通过新颖、省时的学习方法基于数据进行适应来实现。目标是捕捉真实世界交互中固有的模型不确定性；这是一个数据稀缺的过程。为了减少学习所需的数据量，将通过自动发现这些任务和每个任务的潜在运动基元来定制不同的模型。这个任务识别过程将与学习迭代操作，并利用改进的模型来发现新的任务。它还可以为改进的手部设计提供反馈。一旦基于学习和专注于任务的模型可用，它们将被用来学习和合成抓取和手中操作的控制器。为了学习控制器，这项工作将考虑一种基于模型的强化学习方法，该方法将与备选方案进行评估。对于控制器综合，现有的工具将与任务规划原语集成，并通过学习过程进行扩展，以确定不同控制器可以链接在一起的前提条件。该项目包括对PIS实验室设计的各种新型自适应手和机械臂进行广泛的评估。基于视觉的现代解决方案将用于跟踪抓取的对象，并为学习和闭环控制提供反馈。评估将衡量所开发的混合模型是否能够显著提高抓取的稳健性和灵巧操作的有效性。

Robots need to effectively interact with a large variety of objectsthat appear in warehouses and factories as well as homes and offices.This requires robust grasping and dexterous manipulation of everydayobjects through low cost robots and low complexity solutions.Traditionally, robots use rigid hands and analytical models for suchtasks, which often fail in the presence of even small errors. Newcompliant hands promise improved performance, while minimizingcomplexity, and increased robustness. Nevertheless, they areinherently difficult to sense and model. This project combines ideasfrom different robotics sub-fields to address this limitation. Itutilizes progress in machine learning and builds on a strong traditionin robot modeling. The objective is to provide adaptive, compliantrobots that are better in grasping objects in the presence of multipleunknown contact points and sliding or rolling objects in-hand. Thebroader impact will be strengthened by the open release of new ormodified robot hand designs, improved control algorithms and software,as well as corresponding data sets. Furthermore, academicdissemination will be accompanied by educational outreach toundergraduate and high school students.Towards the above objective, the first step will be the definition ofnew hybrid models appropriate for adaptive, compliant hands. Thiswill happen by improving analytical solutions and extending them toallow adaptation based on data via novel, time-efficient learningmethods. The objective is to capture model uncertainty inherent inreal-world interactions; a process that suffers from data scarcity.In order to reduce the amount of data required for learning, differentmodels will be tailored to specific tasks through an automateddiscovery of these tasks and of underlying motion primitives for eachone of them. This task identification process will operate iterativelywith learning and utilize improved models to discover new tasks. Itcan also provide feedback for improved hand design. Once theselearning-based and task-focused models are available, they will beused to learn and synthesize controllers for grasping and in-handmanipulation. To learn controllers, this work will consider amodel-based, reinforcement learning approach, which will be evaluatedagainst alternatives. For controller synthesis, existing tools forthis purpose will be integrated with task planning primitives andextended through learning processes to identify the preconditionsunder which different controllers can be chained together. The projectinvolves extensive evaluation on a variety of novel adaptive hands androbotic arms designed in the PIs' labs. Modern vision-based solutionswill be used to track grasped objects and provide feedback forlearning and closed-loop control. The evaluation will measure whetherthe developed hybrid models can significantly improve robustness ofgrasping and the effectiveness of dexterous manipulation.