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: 2138923
• 负责人: Victoria Webster-Wood
• 金额: $ 41.74万
• 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: 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=2138923&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，如成熟的水果，仍然是机器人技术的一个挑战。柔软的机器人抓手在安全处理这些物体而不损坏它们方面显示出巨大的希望。此外，创建软件来控制软机器人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时都要解决这个问题。它们不仅能够抓住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，并根据先前的经验改变它们在抓取过程中施加的力量。这个项目将从一种有身体的动物——海蛞蝓身上获得灵感，海蛞蝓成功地以各种大小、韧性和形状各异的海藻为食，从而创造出一种新型的软抓取机器人。这个项目还将创造一种机制，可以学习如何安全地抓住各种各样的物体，包括西红柿和蘑菇等易碎的食物。机器人学习如何安全地处理柔软易碎物体的能力将在未来的农业、制造业和医药领域得到应用。该项目还将支持对科学和工程领域多样化劳动力的培训。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，该项目将通过研究生培训、外展活动、本科生暑期研究经历和科学插图实习来支持跨学科培训。该项目将测试具有机载生物启发学习和局部控制的柔性、形态智能抓取机器人的假设，通过快速调整控制器和执行器属性以及实时学习，将提高抓取性能和易用性。为了验证这一假设，该项目将：(1)在短时间尺度上实现执行器的适应性，模拟生物肌肉的短期变化；(2)通过合成神经系统（SNS）的短期学习实现局部控制适应性，模拟生物神经系统的短期网络变化；(3)在合成神经系统（SNS）中实现较长期的突触权重变化，模拟经验学习。在目标1和2，一个生物启发的方法将被应用于开发软抓手的启发加州海蛞蝓（海蛞蝓）喂养。在Aim 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。为了精确地识别网络中需要学习的元素，该项目将在一种可驯服的动物模型——加州阿普里西亚——中研究抓取。这种海蛞蝓善于抓住柔软、易碎、光滑的物体，并能迅速从经验中学习。此外，Aplysia的抓取控制电路只包含几百个神经元，允许在学习过程中测量关键网络元素的特定变化。为了评估抓取生物学原理的价值，该项目将使用人类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。基线数据将用传统的抓取器建立，并将性能进行比较，因为适应性已纳入系统。该项目由跨部门机器人基础研究项目支持，由工程（ENG）和计算机与信息科学与工程（CISE）联合管理和资助。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。

项目成果

Design and Characterization of Viscoelastic McKibben Actuators with Tunable Force-Velocity Curves

具有可调力-速度曲线的粘弹性 McKibben 执行器的设计和表征

• 发表时间: 2023
• 期刊: IEEE International Conference on Soft Robotics
• 作者: Bennington, M.;Wang, T.;Yin, J.;Bergbreiter, S.;Majidi, C.;Webster-Wood, V.
• 通讯作者: Webster-Wood, V.

Synthetic Nervous System Control of a Bioinspired Soft Grasper for Pick-and-Place Manipulation

用于拾放操作的仿生软抓取器的合成神经系统控制

• 发表时间: 2023
• 期刊: Conference on Biomimetic and Biohybrid Systems: Living Machines 2023
• 作者: Sukhnandan, Ravesh;Li, Yanjun;Wang, Yu;Bhammar, Anaya;Dai, Kevin;Bennington, Michael;Chiel, Hillel J;Quinn, Roger D;Webster-Wood, Victoria A
• 通讯作者: Webster-Wood, Victoria A

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.