Robots that can effectively understand human intentions from actions are crucial for successful human-robot collaboration. In this work, we address the challenge of a robot navigating towards an unknown goal while also accounting for a human's preference for a particular path in the presence of obstacles. This problem is particularly challenging when both the goal and path preference are unknown a priori. To overcome this challenge, we propose a method for encoding and inferring path preference online using a partitioning of the space into polytopes. Our approach enables joint inference over the goal and path preference using a stochastic observation model for the human. We evaluate our method on an unknown-goal navigation problem with sparse human interventions, and find that it outperforms baseline approaches as the human's inputs become increasingly sparse. We find that the time required to update the robot's belief does not increase with the complexity of the environment, which makes our method suitable for online applications.

翻译：能够从行为中有效理解人类意图的机器人对于成功的人机协作至关重要。本研究针对机器人在障碍环境中向未知目标导航时兼顾人类对特定路径偏好的挑战。当目标与路径偏好均先验未知时，该问题尤为困难。为应对这一挑战，我们提出一种基于空间凸多面体划分的在线路径偏好编码与推断方法。该方法通过构建人类随机观测模型，实现对目标与路径偏好的联合推断。我们在稀疏人类干预的未知目标导航问题上验证了该方法，结果表明当人类输入日益稀疏时，本方法优于基线方法。值得注意的是，机器人信念更新所需时间不随环境复杂度增加而增长，这使该方法适用于在线应用场景。