To be useful teammates to human partners, robots must be able to follow spoken instructions given in natural language. However, determining the correct sequence of actions in response to a set of spoken instructions is a complex decision-making problem.  There is a "semantic gap" between the high-level symbolic models of the world that people use, and the low-level models of geometry, state dynamics, and perceptions that robots use. In this paper, we show how this gap can be bridged by inferring the best sequence of actions from a linguistic description and environmental features.  This work improves upon previous work in three ways.  First, by using a conditional random field (CRF), we learn the relative weight of environmental and linguistic features, enabling the system to learn the meanings of words and reducing the modeling effort in learning how to follow commands.  Second, a number of long-range features are added, which help the system to use additional structure in the problem. Finally, given a natural language command, we infer both the referred path and landmark directly, thereby requiring the algorithm to pick a landmark by which it should navigate.  The CRF is demonstrated to have 15% error on a held-out dataset, when compared with 39% error for a Markov random field (MRF).  Finally, by analyzing the additional annotations necessary for this work, we find that natural language route directions map sequentially onto the corresponding path and landmarks 99.6% of the time.  In addition, the size of the referred landmark varies from 0m 2  to 1964m 2  and the length of the referred path varies from 0 m  to 40.83 m .
['Thomas Kollar', 'Stefanie Tellex', 'Nicholas Roy']
A Discriminative Model for Understanding Natural Language Route Directions