Where to go: Interpreting natural directions using global inference
Name
Wei-2009-Where to go Interpreting natural directions using global inference.pdf
Size
559.59 KB
Format
Adobe PDF
Checksum (MD5)
b8f187f041eeab35304f085b5ed0c68c
Author(s)
Wei, Yuan
Brunskill, Emma
Kollar, Thomas Fleming
Roy, Nicholas
Date Issued
May 2009
Journal
IEEE International Conference on Robotics and Automation, 2009. ICRA '09
Publisher
Institute of Electrical and Electronics Engineers
Citation
Yuan Wei et al. “Where to go: Interpreting natural directions using global inference.” Robotics and Automation, 2009. ICRA’09. IEEE International Conference on. 2009. 3761-3767. © 2009 IEEE.
Version
Final published version
Abstract
An important component of human-robot interaction is that people need to be able to instruct robots to move to other locations using naturally given directions. When giving directions, people often make mistakes such as labelling errors (e.g., left vs. right) and errors of omission (skipping important decision points in a sequence). Furthermore, people often use multiple levels of granularity in specifying directions, referring to locations using single object landmarks, multiple landmarks in a given location, or identifying large regions as a single location. The challenge is to identify the correct path to a destination from a sequence of noisy, possibly erroneous directions. In our work we cast this problem as probabilistic inference: given a set of directions, an agent should automatically find the path with the geometry and physical appearance to maximize the likelihood of those directions. We use a specific variant of a Markov Random Field (MRF) to represent our model, and gather multi-granularity representation information using existing large tagged datasets. On a dataset of route directions collected in a large third floor university building, we found that our algorithm correctly inferred the true final destination in 47 out of the 55 cases successfully followed by humans volunteers. These results suggest that our algorithm is performing well relative to human users. In the future this work will be included in a broader system for autonomously constructing environmental representations that support natural human-robot interaction for direction giving.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ROBOT.2009.5152775
