RI: Medium: Robust Models and Physical Interactions for Managing Specialty Crops

RI：中：管理特种作物的稳健模型和物理相互作用

• 批准号: 1956163
• 负责人: Oliver Kroemer
• 金额: $ 119.99万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1956163&HistoricalAwards=false
• 关键词: RI; Medium; Robust; Models; Physical

This grant supports research into developing robots that can reliably and robustly interact with plants to assist in the managing and harvesting of specialty crops. Managing specialty crops, such as fruits and nuts, requires a considerable amount of dexterity and skill. To maximize yields, these crops need to be monitored and pruned regularly throughout the year before being harvested. These tasks are labor intensive and not amenable to automation via the traditional mechanization methods used for broadacre crops such as corn and soybeans. An increasing labor shortage is thus threatening the US specialty crop industry. Intelligent automation presents a promising approach to addressing this issue, with robots performing the uncomfortable and dangerous farm work. However, performing such complex tasks quickly and reliably in unstructured environments is beyond the capabilities of current robots. In this project, a team of researchers will develop a framework for robots to reliably model and manipulate specialty crops in a robust manner. New perception algorithms will allow robots to use vision and touch to identify the different parts of the plants and their connections. New controllers and planning algorithms will allow robots to reach deep into the canopies of plants to reliably prune, push aside, or harvest specific parts of the plants. The developed methods will not only provide support for automating the farming of specialty crops, but also techniques for creating more accurate models of these plants for long-term monitoring and phenotyping. The team of researchers will address the challenges of managing and harvesting specialty crops by advancing the state of the art in modeling and manipulating flexible objects. The researchers will develop perception and multi-layer modeling techniques to capture the scenes’ 3D geometry and physical properties. The resulting models will capture the physical connections within the scenes as well as model the uncertainty for these high-occlusion environments. The team will create algorithms for planning and executing safe interactions with the cluttered and constrained environments. The research will also include the development of interactive perception methods for improving the scene models based on experiences from interacting with the specialty crops. Research contributions to perception, planning, and modeling will all be extensively evaluated on real robots both in the lab and in the field.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.

该补助金支持开发机器人的研究，这些机器人可以可靠和强大地与植物互动，以协助管理和收获特种作物。管理水果和坚果等特种作物需要相当多的灵活性和技能。为了最大限度地提高产量，这些作物需要在收获前进行全年定期监测和修剪。这些任务是劳动密集型的，不适合通过用于玉米和大豆等大田作物的传统机械化方法进行自动化。因此，日益严重的劳动力短缺威胁着美国特种作物产业。智能自动化为解决这一问题提供了一种很有前途的方法，机器人可以执行不舒服和危险的农场工作。然而，在非结构化环境中快速可靠地执行如此复杂的任务超出了当前机器人的能力。在这个项目中，一组研究人员将开发一个框架，让机器人以稳健的方式可靠地建模和操纵特种作物。新的感知算法将允许机器人使用视觉和触觉来识别植物的不同部分及其连接。新的控制器和规划算法将允许机器人深入植物的树冠，以可靠地修剪、推开或收获植物的特定部分。开发的方法不仅将为特种作物的自动化种植提供支持，还将为这些植物的长期监测和表型分析提供更准确的模型。研究团队将通过推进建模和操纵柔性物体的最新技术来解决管理和收获特种作物的挑战。研究人员将开发感知和多层建模技术，以捕捉场景的3D几何形状和物理特性。生成的模型将捕获场景中的物理连接，并为这些高遮挡环境的不确定性建模。该团队将创建算法，用于规划和执行与混乱和受限环境的安全交互。该研究还将包括开发交互式感知方法，以根据与特种作物交互的经验改进场景模型。感知、规划和建模方面的研究贡献都将在实验室和现场的真实的机器人上进行广泛评估。该奖项反映了NSF的法定使命，并且通过使用基金会的智力价值和更广泛的影响力进行评估，被认为值得支持。审查标准。

项目成果

Learning Reactive and Predictive Differentiable Controllers for Switching Linear Dynamical Models

学习用于切换线性动态模型的反应性和预测性微分控制器

• 发表时间: 2021
• 期刊: IEEE International Conference on Robotics and Automation
• 作者: Saxena, S.;LaGrassa, A.;Kroemer, O.
• 通讯作者: Kroemer, O.

Search-Based Task Planning with Learned Skill Effect Models for Lifelong Robotic Manipulation

基于搜索的任务规划与终身机器人操作的学习技能效果模型

• DOI: 10.1109/icra46639.2022.9811575
• 发表时间: 2022
• 期刊: International Conference on Robotics and Automation (ICRA
• 作者: Liang, Jacky;Sharma, Mohit;LaGrassa, Alex;Vats, Shivam;Saxena, Saumya;Kroemer, Oliver
• 通讯作者: Kroemer, Oliver

Learning Model Preconditions for Planning with Multiple Models

使用多个模型进行规划的学习模型先决条件

• 发表时间: 2022
• 期刊: 5th Conference on Robot Learning
• 作者: LaGrassa, Alex;Kroemer, Oliver
• 通讯作者: Kroemer, Oliver

Generalizing Object-Centric Task-Axes Controllers using Keypoints

使用关键点泛化以对象为中心的任务轴控制器

• 发表时间: 2021
• 期刊: IEEE International Conference on Robotics and Automation
• 作者: Sharma, M.;Kroemer, O.
• 通讯作者: Kroemer, O.

Focused Adaptation of Dynamics Models for Deformable Object Manipulation

• DOI: 10.1109/icra48891.2023.10161366
• 发表时间: 2022-09
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: P. Mitrano;A. LaGrassa;Oliver Kroemer;D. Berenson