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

这个国家机器人计划（NRI）项目的目标是使人类和机器人的混合团队能够在复杂的环境中完成对物理要求很高的物体操作任务。在这个项目中，软机器人被专门考虑，因为传统机器人太重，与人密切合作有潜在的危险。人类可以有效地合作移动笨重的物体，因为他们能够利用对群体目标和个人能力的理解来解释物理线索并快速推断彼此的意图。因此，将这种能力扩展到机器人的第一步是了解群体如何识别和应对其他团队成员的推拉。该项目还强调了在与软机器人和人一起工作时管理不确定性的必要性-软机器人是因为它们在典型的任务负载下会显著变形，而人是因为它们的运动可能很难被机器人预测。该研究的潜在应用范围可以从加快物流和材料处理，到改善危险和/或难以到达的环境中的人类安全，如采矿，石油钻井平台，伐木和搜索和救援。为此，与当地搜索和救援团队的合作将在整个项目中征求关于人机协同操作的反馈。代表性不足的本科生将接受利用软机器人技术的STEM教育工具的培训，然后学生们将努力将这种培训传播到当地的K-12教室。协同操作可以定义为在移动单个大对象时，由多个协作代理所采取的动作和发送的信号。这项研究将使人类和机器人之间的协同操作，并集中在以下三个主要方面：1）建模，控制和规划软机器人的有效刚度轨迹，以处理任务的不确定性，2）量化和建模人类的意图和共识在操作过程中，和3）开发算法，将意图，共识和不确定性结合起来执行协同操作任务。基于大自由度软机器人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生成刚度轨迹作为软机器人控制的一部分。人类合作者在真实的生活和虚拟现实中移动大型物体的试验将允许开发预测群体共识和运动的算法。最后，给出一个合理的估计的短期运动目标的一组，由此产生的算法也将产生机器人的运动和刚度轨迹，以帮助一组更有效地达成共识，通过减少不确定性。这项研究将开创自然物理交互、安全机器人控制、多智能体协调和分布式规划/行动的新组合。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.