NRI: FND: Foundations for Physical Co-Manipulation with Mixed Teams of Humans and Soft Robots

NRI：FND：人类和软机器人混合团队物理协同操作的基础

• 批准号: 2024670
• 负责人: Rebecca Kramer-Bottiglio
• 金额: $ 3.19万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024670&HistoricalAwards=false
• 关键词: NRI; FND; Foundations; Physical; Co

The goal of this National Robotics Initiative (NRI) project is to enable mixed teams of humans and robots to work together to accomplish physically demanding object manipulation tasks in complex environments. For this project, soft robots are exclusively considered, because traditional robots are too heavy and potentially dangerous to work closely with people. Humans can effectively work together to move a bulky, heavy object because they are able to use their understanding of group goals and individual capabilities to interpret physical cues and quickly infer each other's intention. Thus, the first step in extending this ability to robots is to understand how groups of people recognize and react to pushing and pulling from other team members. The project also emphasizes the necessity of managing uncertainty when working with soft robots and with people -- soft robots because they deform significantly under typical task loads, and people because their movements may be difficult for robots to predict. Potential applications of the research can range from expediting logistics and material handling, to improving human safety in dangerous and/or hard-to-reach environments such as mining, oil rigs, logging, and search and rescue. To this end, a collaboration with a local search and rescue team will solicit feedback on human-robot co-manipulation throughout the project. Underrepresented undergraduate students will be trained with a STEM education tool leveraging soft robotics, and the students will then work to disseminate this training to local K-12 classrooms. Co-manipulation can be defined as the actions taken and the signals sent by many collaborating agents while moving a single large object. This research will enable co-manipulation between humans and robots, and is focused on the following three main thrusts: 1) modeling, controlling, and planning effective stiffness trajectories for soft robots to deal with task uncertainty, 2) quantifying and modeling human intention and consensus during manipulation, and 3) developing algorithms that incorporate intention, consensus, and uncertainty to execute co-manipulation tasks. Building on prior work on model predictive control algorithms for large-degree-of-freedom soft robots, stiffness trajectories will be generated as part of the soft robot control based on estimates of task uncertainty. Trials with human collaborators moving large objects in real life and in virtual reality will allow the development of algorithms that predict consensus and motion of the group. Finally, given a reasonable estimate of the short-term motion goal of a group, the resulting algorithms will also generate robot motion and stiffness trajectories to help a group reach consensus more efficiently by reducing uncertainty. This research will pioneer the novel combination of natural physical interaction, control for safe robots, multi-agent coordination, and planning/acting in a distributed manner.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.

这项国家机器人倡议（NRI）项目的目标是使混合的人类和机器人团队共同努力，在复杂的环境中完成对物理要求的物体操纵任务。对于这个项目，仅考虑软机器人，因为传统机器人太重和潜在的危险，无法与人紧密合作。人类可以有效地共同努力，以移动一个笨重的重物，因为他们能够利用对小组目标和个人能力的理解来解释身体线索并迅速推断彼此的意图。因此，将这种能力扩展到机器人的第一步是了解人们如何认识和反应其他团队成员。该项目还强调了与软机器人和人一起工作时要管理不确定性的必要性 - 软机器人，因为它们在典型的任务负载下显着变形，而人们则因为他们的运动可能难以预测机器人。研究的潜在应用可以从加快物流和物料处理，到在危险和/或难以到达的环境中改善人体安全，例如采矿，石油钻机，伐木以及搜索和救援。为此，与当地搜救团队的合作将在整个项目中征求有关人类与人类共同操作的反馈。代表性不足的本科生将接受STEM教育工具的培训，以利用软机器人技术，然后学生将努力将这项培训传播给当地的K-12教室。共同操作可以定义为在移动单个大对象时，许多协作代理商发送的动作和信号。 This research will enable co-manipulation between humans and robots, and is focused on the following three main thrusts: 1) modeling, controlling, and planning effective stiffness trajectories for soft robots to deal with task uncertainty, 2) quantifying and modeling human intention and consensus during manipulation, and 3) developing algorithms that incorporate intention, consensus, and uncertainty to execute co-manipulation tasks.基于对大型自由度软机器人的模型预测控制算法的先前工作，将根据任务不确定性的估计，将生成刚度轨迹作为软机器人控制的一部分。与人类合作者在现实生活中和虚拟现实中移动大型对象的试验将使人们开发预测小组共识和运动的算法。最后，鉴于对组的短期运动目标的合理估计，所得算法还将产生机器人运动和刚度轨迹，以帮助组通过降低不确定性更有效地达成共识。这项研究将开创自然物理互动，安全机器人控制，多机构协调和计划/行动的新型组合。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估。