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：能够有效理解人类行为意图的机器人对于成功的人机协作至关重要。本文针对机器人在未知目标导航过程中，同时需要考虑人类在障碍物环境中对特定路径偏好的挑战展开研究。当目标和路径偏好均先验未知时，该问题尤为困难。为应对这一挑战，我们提出了一种基于空间划分多面体的在线路径偏好编码与推断方法。该方法通过人类随机观测模型，实现了对目标和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评估了该方法，结果表明随着人类输入愈发稀疏，该方法优于基线方法。研究发现，更新机器人信念所需时间不受环境复杂度影响，这使得该方法适用于在线应用场景。