Collaborative Research: FRR: Adaptive mechanics, learning and intelligent control improve soft robotic grasping

合作研究：FRR：自适应力学、学习和智能控制改善软机器人抓取

• 批准号: 2138873
• 负责人: Roger Quinn
• 金额: $ 81.66万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138873&HistoricalAwards=false
• 关键词: Collaborative; Research; FRR; Adaptive; mechanics

Handling soft, fragile, or slippery objects such as ripe fruit remains a challenge in robotics. Soft robotic graspers show tremendous promise in safely handling such objects without damaging them. Furthermore, creating software to control soft robots poses an additional challenge. In contrast, many animals with soft bodies solve this problem everyday as they forage and feed. Not only are they able to grasp and manipulate soft and fragile objects, but animals can also learn how to safely interact with new objects and vary how much force they apply during grasping based on their prior experience. This project will take inspiration from an animal with a body, the sea slug, that feeds successfully on a range of seaweeds that vary greatly in size, toughness and shape, to create new type of soft grasping robot. This project will also create a mechanism that can learn how to safely grasp a wide range of objects, including fragile foods like tomatoes and mushrooms. The ability for a robot to learn how to safely handle soft and fragile objects will have future applications in agriculture, manufacturing, and medicine. This project will also support the training of a diverse workforce in science and engineering. Students from grade school through college will be included as research participants to test the robot. Additionally, this project will support cross-disciplinary training through graduate student training, outreach activities, summer research experiences for undergraduates, and internships in scientific illustration.This project will test the hypothesis that soft, morphologically intelligent grasping robots with onboard bioinspired learning and local control will improve grasping performance and ease of use by rapidly adjusting controller and actuator properties and learning in real-time. To test this hypothesis, this project will: (1) implement actuator adaptability over short timescales, mimicking short-term changes in biological muscle, (2) implement local control adaptability through short-term learning in a synthetic nervous system (SNS), mimicking short-term network changes in biological neural systems, and (3) implement longer-term synaptic weight changes in an SNS, mimicking learning from experience. In Aims 1 and 2, a bioinspired approach will be applied to develop a soft grasper inspired by Aplysia californica (sea slug) feeding. In Aim 3, this approach will be extended to a robot arm and long-term learning will be incorporated into the controller. To precisely identify elements of the network subject to learning, this project will study grasping in a tractable animal model, Aplysia californica. This marine sea slug is adept at grasping soft, fragile, slippery objects and rapidly learns with experience. Furthermore, Aplysia’s grasping control circuitry contains only a few hundred neurons, allowing the measurement of specific changes in key network elements during learning. To assess the value of biological principles for grasping, this project will use human subjects to measure the robotic grasper’s performance, ease of use, and operator training time. Baseline data will be established with a conventional grasper and performance will be compared as adaptability is integrated into the system.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.

处理柔软、易碎或光滑的物体（例如成熟的水果）仍然是机器人技术中的一个挑战。软机器人抓取器在安全处理此类物体而不损坏它们方面显示出巨大的前景。此外，创建控制软机器人的软件也带来了额外的挑战。相比之下，许多身体柔软的动物每天在觅食和进食时都会解决这个问题。它们不仅能够抓握和操纵柔软易碎的物体，而且还可以学习如何安全地与新物体互动，并根据之前的经验改变抓握过程中施加的力量。该项目将从一种有身体的动物——海蛞蝓中获得灵感，这种动物成功地以一系列尺寸、韧性和形状各不相同的海藻为食，创造出新型的软抓取机器人。该项目还将创建一种机制，可以学习如何安全地抓取各种物体，包括西红柿和蘑菇等易碎食物。机器人学习如何安全处理柔软易碎物体的能力将在农业、制造业和医学领域得到应用。该项目还将支持科学和工程领域多元化劳动力的培训。从小学到大学的学生都将作为研究参与者来测试机器人。此外，该项目还将通过研究生培训、外展活动、本科生暑期研究经历以及科学插图实习来支持跨学科培训。该项目将测试这样的假设：具有机载仿生学习和本地控制功能的软形态智能抓取机器人将通过快速调整控制器和执行器属性和实时学习来提高抓取性能和易用性。为了检验这一假设，该项目将：（1）在短时间内实现执行器适应性，模仿生物肌肉的短期变化，（2）通过合成神经系统（SNS）中的短期学习实现局部控制适应性，模仿生物神经系统中的短期网络变化，以及（3）在SNS中实现长期突触权重变化，模仿经验学习。在目标 1 和 2 中，将采用仿生方法来开发受海兔（海蛞蝓）喂养启发的软抓握器。在目标 3 中，这种方法将扩展到机器人手臂，并将长期学习纳入控制器中。为了精确识别需要学习的网络元素，该项目将研究一种易于驯服的动物模型——海兔（Aplysia californica）的抓握行为。这种海洋海蛞蝓善于抓住柔软、易碎、光滑的物体，并能通过经验快速学习。此外，海兔的抓取控制电路仅包含数百个神经元，可以测量学习过程中关键网络元素的特定变化。为了评估生物学原理对于抓取的价值，该项目将使用人体受试者来测量机器人抓取器的性能、易用性和操作员培训时间。将使用传统抓取器建立基线数据，并在将适应性集成到系统中时对性能进行比较。该项目得到了机器人学跨部门基础研究计划的支持，该计划由工程理事会 (ENG) 和计算机与信息科学与工程理事会 (CISE) 共同管理和资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和技术进行评估，认为值得支持。 更广泛的影响审查标准。

项目成果

A Bioinspired Synthetic Nervous System Controller for Pick-and-Place Manipulation

用于拾放操作的仿生合成神经系统控制器

• DOI: 10.1109/icra48891.2023.10161198
• 发表时间: 2023
• 期刊: IEEE
• 作者: Li, Yanjun;Sukhnandan, Ravesh;Gill, Jeffrey P.;Chiel, Hillel J.;Webster-Wood, Victoria;Quinn, Roger D.
• 通讯作者: Quinn, Roger D.