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、家庭和办公室中出现的各种各样的物体进行交互。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物体进行强大的抓取和灵巧的操作。传统上，机器人使用刚性的手和分析模型来完成这些任务，即使是很小的错误也经常会失败。新兼容的手承诺提高性能，同时最小化复杂性，并增加鲁棒性。然而，它们本身就难以感知和建模。这个项目结合了不同机器人子领域的想法来解决这个限制。它利用了机器学习方面的进展，并建立在强大的机器人建模传统之上。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，更好地抓取物体。通过公开发布新的或修改的机械手设计，改进的控制算法和软件以及相应的数据集，将加强更广泛的影响。此外，学术传播将伴随着对大学生和高中生的教育推广。为了实现上述目标，第一步将是定义适合自适应、顺应的手的新混合模型。这将通过改进分析解决方案和扩展它们来实现，以便通过新颖、省时的学习方法基于数据进行适应。目标是捕获模型的不确定性固有的在现实世界的相互作用；一个遭受数据短缺的过程。为了减少学习所需的数据量，不同的模型将通过自动发现这些任务和每个任务的潜在运动原语来定制特定的任务。这个任务识别过程将随着学习和利用改进的模型来发现新任务而迭代地操作。它还可以为改进手的设计提供反馈。一旦这些基于学习和以任务为中心的模型可用，它们将用于学习和合成用于抓取和手持操作的控制器。为了学习控制器，这项工作将考虑基于模型的强化学习方法，该方法将根据备选方案进行评估。对于控制器综合，用于此目的的现有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。该项目涉及对pi实验室设计的各种新型自适应手和机械臂进行广泛评估。现代基于视觉的解决方案将用于跟踪抓取的物体，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显著提高抓取的鲁棒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.