Navigating to destinations using human speech instructions is an important
task for autonomous mobile robots that operate in the real world. Spatial
representations include a semantic level that represents an abstracted location
category, a topological level that represents their connectivity, and a metric
level that depends on the structure of the environment. The purpose of this
study is to realize a hierarchical spatial representation using a topometric
semantic map and planning efficient paths through human-robot interactions. We
propose a novel probabilistic generative model, SpCoTMHP, that forms a
topometric semantic map that adapts to the environment and leads to
hierarchical path planning. We also developed approximate inference methods for
path planning, where the levels of the hierarchy can influence each other. The
proposed path planning method is theoretically supported by deriving a
formulation based on control as probabilistic inference. The navigation
experiment using human speech instruction shows that the proposed spatial
concept-based hierarchical path planning improves the performance and reduces
the calculation cost compared with conventional methods. Hierarchical spatial
representation provides a mutually understandable form for humans and robots to
render language-based navigation tasks feasible.

利用语音指令完成机器人自主导航的一个重要任务是构建具备层级空间表示能力的智能移动机器人。为此，研究者提出了一种基于概率生成模型的层级空间表示方法 SpCoTMHP，并相应的提出了一种路径规划方法，实现了机器人与人类之间的交互通信，提高了导航性能和降低了计算成本。