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Author Hemachandra, S. ♦ Duvallet, F. ♦ Howard, T.M. ♦ Roy, N. ♦ Stentz, A. ♦ Walter, M.R.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Other branches of engineering
Subject Keyword Natural languages ♦ Semantics ♦ Robot sensing systems ♦ Grounding ♦ Topology
Abstract Natural language offers an intuitive and flexible means for humans to communicate with the robots that we will increasingly work alongside in our homes and workplaces. Recent advancements have given rise to robots that are able to interpret natural language manipulation and navigation commands, but these methods require a prior map of the robot's environment. In this paper, we propose a novel learning framework that enables robots to successfully follow natural language route directions without any previous knowledge of the environment. The algorithm utilizes spatial and semantic information that the human conveys through the command to learn a distribution over the metric and semantic properties of spatially extended environments. Our method uses this distribution in place of the latent world model and interprets the natural language instruction as a distribution over the intended behavior. A novel belief space planner reasons directly over the map and behavior distributions to solve for a policy using imitation learning. We evaluate our framework on a voice-commandable wheelchair. The results demonstrate that by learning and performing inference over a latent environment model, the algorithm is able to successfully follow natural language route directions within novel, extended environments.
Description Author affiliation: Robot. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA (Duvallet, F.; Stentz, A.) || Univ. of Rochester, Rochester, NY, USA (Howard, T.M.) || Toyota Technol. Inst. at Chicago, Chicago, IL, USA (Walter, M.R.) || Comput. Sci. & Artificial Intell. Lab., Massachusetts Inst. of Technol., Cambridge, MA, USA (Hemachandra, S.; Roy, N.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-05-26
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781479969234
Size (in Bytes) 2.99 MB
Page Count 8
Starting Page 5608
Ending Page 5615


Source: IEEE Xplore Digital Library