I-Corps: Healthcare Service Robots

过去机器人技术的商业化努力一直由移动的基地主导。机器人手臂倾向于呆在笼子里或远离人类（例如，制造）。这个I-Corps团队希望将移动的机械手带到人类环境中，以执行人们不希望的重复性手动任务。该团队最初的假设是，表面清洁就是这样一项任务。预计地面卫生自动化可以实现更高频率的更大表面覆盖率，创造更卫生的环境，减少疾病。这样的机器人也有可能自动化不受欢迎的体力劳动，以及减少与重复体力劳动相关的人类伤害。自动化需要较低技能或体力劳动的任务可以通过让人类更多地关注高技能劳动力来缓解劳动力短缺。 因此，这个项目的目的是定义一个智能移动的机器人的商业应用。无处不在的、友好的机器人的存在，可以被更多的人使用，理想情况下也会激励年轻一代追求STEM教育，从而导致美国劳动力更加熟练和有竞争力。本计画的目的是定义一个商业应用的智能移动的机器人与手臂（移动的机械手）。基于八年多来在人机交互方面的研究成果，该团队打算应用从演示中学习（LfD）算法，使机器人可由没有机器人经验的最终用户编程，这可以通过直观的社交对话来实现。机器人还需要在人类在场的情况下以透明和可预测的方式自主行动，这需要开发具有社会意识的操纵和导航行为。在I-Corps计划期间，该团队的大部分方法将致力于客户发现访谈，最初的目标客户是医疗保健机构。为了验证这一细分市场，该团队打算访问医院和长期护理机构，以确定客户的痛点，以及采用机器人技术的流程和障碍。此外，该团队计划采访专家，参加会议，并进行文献检索，以更好地了解该领域;并且，在I-Corps计划结束时，它预计已经定义了一系列机器人必须执行的关键任务，以便为我们的目标客户群提供价值。这些任务将为机器人硬件和软件的最小可行设计定义规范。

Past commercialization efforts for robotics have been dominated by mobile bases. Robotic arms tend to stay in cages or far from humans (e.g., manufacturing). This I-Corps team wants to bring mobile manipulators to human environments to perform repetitive manual tasks that people find undesirable. The team's initial hypothesis is that surface cleaning is one such task. It is anticipated that automation of surface sanitation can achieve more surface coverage with higher frequency, creating a more sanitary environment that reduces illness. Such a robot also has the potential to automate undesirable manual labor, as well as reduce human injuries associated with repetitive physical work. Automating tasks requiring lower skills or manual labor could ameliorate the labor shortage by allowing humans to focus more on high-skilled labor. This project, therefore, aims to define a commercial application for an intelligent mobile robot. The presence of ubiquitous, friendly robots that can be used by more people will ideally also inspire younger generations to pursue STEM education, leading to a more skilled and competitive American workforce. This project aims to define a commercial application for an intelligent mobile robot with an arm (mobile manipulator). Based on over eight years of research results in Human-Robot Interaction, the team intends to apply learning from demonstration (LfD) algorithms to make the robot programmable by end users with no robotics experience, which can be accomplished using an intuitive social dialogue. The robot will also need to act autonomously in the presence of humans in a way that is transparent and predictable, which requires developing manipulation and navigation behavior with social awareness. The bulk of the team's methodology during the I-Corps program will be dedicated to interviews for customer discovery, with an initial target customer as health care establishments. To validate this segment, the team intends to visit hospitals and long-term care facilities to identify customer pain points, as well as processes and barriers for adopting robotic technology. In addition, the team plans to interview experts, attend meetings, and conduct literature searches to better understand the domain; and, by the end of the I-Corps program, it expects to have defined a set of key tasks that the robot must perform to provide value to our target customer segment. These tasks will define specifications for a minimum viable design of the robot hardware and software.