We focus on the problem of how we can enable a robot to collaborate seamlessly with a human partner, specifically in scenarios where preexisting 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 safe trajectory 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：本文研究如何使机器人在数据稀疏场景下与人类伙伴实现无缝协作。现有的人机协作研究大多采用对人类行为的观测模型（即仅将机器人视为观察者）来选择机器人行为，但此类模型未考虑机器人对人类行为的影响，可能导致交互效率低下。为此，我们提出基于条件行为预测的协作机器人行为优化框架，该框架通过建立依赖机器人未来行为的人类条件模型来实现决策。首先，我们提出一种基于模型的条件行为预测方法，使机器人能在数据稀疏环境中根据自身未来计划推断人类意图。随后，我们展示如何利用条件模型进行主动目标选择，并在人类协作对象周围生成安全轨迹。最后，通过真实用户的协作任务实验表明，我们提出的主动控制器能在定量与定性层面改善用户与协作机器人的交互体验。