We focus on the problem of how we can enable a robot to collaborate seamlessly with a human partner, specifically in scenarios like collaborative manufacturing where prexisting data is sparse. Much prior work in human-robot collaboration uses observational models of humans (i.e. models that treat the robot purely as an observer) to choose the robot's behavior, but such models do not account for the influence the robot has on the human's actions, which may lead to inefficient interactions. We instead formulate the problem of optimally choosing a collaborative robot's behavior based on a conditional model of the human that depends on the robot's future behavior. First, we propose a novel model-based formulation of conditional behavior prediction that allows the robot to infer the human's intentions based on its future plan in data-sparse environments. We then show how to utilize a conditional model for proactive goal selection and path generation around human collaborators. Finally, we use our proposed proactive controller in a collaborative task with real users to show that it can improve users' interactions with a robot collaborator quantitatively and qualitatively.

翻译：我们聚焦于如何使机器人与人类伙伴无缝协作的问题，特别是在协作制造等预先数据稀少的场景中。以往许多人机协作研究采用人类观察模型（即仅将机器人视为观察者的模型）来选择机器人的行为，但这类模型未考虑机器人对人类行为的影响，可能导致低效交互。因此，我们基于一个依赖于机器人未来行为的条件性人类模型，将最优选择协作机器人行为的问题形式化。首先，我们提出一种新颖的基于模型的条件行为预测形式化方法，使机器人能够在数据稀疏环境中根据其未来计划推断人类意图。随后，我们展示了如何利用条件性模型实现围绕人类协作伙伴的主动目标选择与路径生成。最后，我们在与真实用户协作的任务中应用所提出的主动控制器，证明其能在定量与定性层面改善用户与协作机器人的交互体验。