Before revision: We present a system that enables robotsto interpret spatial language as a distribution over object locations for effective search in partially observable cityscale environments. We introduce the spatial language observation space and formulate a stochastic observation model under the framework of Partially Observable Markov Decision Process (POMDP) which incorporates information extracted from the spatial languageinto the robot's belief.
After revision: We present a system that enables robotsto interpret spatial language as goal specification for instruction following tasks in fully observable domains, often paired with reference paths for reward-based learning. However, spatial language is inherently subjective and potentially ambiguous or misleading. Hence, in this paper, we consider spatial language as a form of stochastic observation. We propose SLOOP (Spatial Language Object-Oriented POMDP), a new framework for partially observable decision making with a probabilistic observation model for spatial language. We apply SLOOP to object search in city-scale environments.