基于多模态感知学习社交机器人导航的研究
A Study on Learning Social Robot Navigation with Multimodal Perception
AI总结：
本文基于大规模真实世界数据集，系统研究了多模态感知在社交机器人导航学习中的作用，在全局与局部规划层面对比单模态、多模态及经典导航方法，并通过人类研究证明多模态学习在社会合规性上具有明显优势。
摘要：
自主移动机器人需要通过机载传感器（如LiDAR和RGB相机）感知环境，然后做出适当的导航决策。为了在人类居住的公共空间中导航，这样的导航任务不仅仅是避障，还需要考虑周围人类及其意图，以便根据潜在的社会规范在一定程度上改变导航行为，即具备社会合规性。机器学习方法已被证明能够以数据驱动的方式捕捉那些复杂而微妙的社会交互，而无需手工设计简化模型或成本函数。考虑到多种可用的传感器模态以及学习方法的效率，本文使用大规模真实世界数据集，对基于多模态感知学习社交机器人导航进行了全面研究。该研究在全局和局部规划两个层面考察了社交机器人导航决策，并在不同社交场景中将单模态学习和多模态学习与一组经典导航方法进行对比，同时从学习角度分析了训练性能和泛化性能。我们还开展了一项人类研究，探讨使用多模态感知进行学习如何影响感知到的社会合规性。结果表明，在数据集研究和人类研究中，多模态学习都明显优于单模态学习。我们开源了代码，供社区未来研究多模态感知以学习社交机器人导航时使用。
英文摘要：
Autonomous mobile robots need to perceive the environments with their onboard sensors (e.g., LiDARs and RGB cameras) and then make appropriate navigation decisions. In order to navigate human-inhabited public spaces, such a navigation task becomes more than only obstacle avoidance, but also requires considering surrounding humans and their intentions to somewhat change the navigation behavior in response to the underlying social norms, i.e., being socially compliant. Machine learning methods are shown to be effective in capturing those complex and subtle social interactions in a data-driven manner, without explicitly hand-crafting simplified models or cost functions. Considering multiple available sensor modalities and the efficiency of learning methods, this paper presents a comprehensive study on learning social robot navigation with multimodal perception using a large-scale real-world dataset. The study investigates social robot navigation decision making on both the global and local planning levels and contrasts unimodal and multimodal learning against a set of classical navigation approaches in different social scenarios, while also analyzing the training and generalizability performance from the learning perspective. We also conduct a human study on how learning with multimodal perception affects the perceived social compliance. The results show that multimodal learning has a clear advantage over unimodal learning in both dataset and human studies. We open-source our code for the community's future use to study multimodal perception for learning social robot navigation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
