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Cybernetics / Learning / Machine learning / Algorithm / Inference / Causality / Logic / Knowledge / Science


Learning Models for Following Natural Language Directions in Unknown Environments Sachithra Hemachandra∗ Felix Duvallet∗ Thomas M. Howard Nicholas Roy Anthony Stentz Matthew R. Walter Abstract—Natural language offe
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Document Date: 2015-03-18 15:34:11

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File Size: 2,97 MB

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City

Multiclass Kernel / /

Company

Autonomous Systems / U.S. Army Research Laboratory / Artificial Intelligence Laboratory / /

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Facility

Toyota Technological Institute / Carnegie Mellon University / University of Rochester / U.S. Army Research Laboratory / Robotics Institute / Massachusetts Institute of Technology / hall Fig / /

IndustryTerm

estimation-theoretic algorithm / Online Learning / approximate solution / manufacturing / healthcare / semantic map inference algorithm / /

Organization

Robotics Institute / United States Army / University of Rochester / Rochester / MIT’s Stata Center / VP PP / Toyota Technological Institute / Carnegie Mellon University / Pittsburgh / Massachusetts Institute of Technology / /

Person

S. Teller / Matthew R. Walter / T. M. Howard / S. Hemachandra / Howard Nicholas Roy Anthony / S. Tellex / A. G. Banerjee / O. Propp / F. Duvallet / Nat / J. Oh / M. R. Walter / T. Howard / R. Walter Abstract / A. Stentz / B. Homberg / N. Roy / I. Chung / T. Kollar / D. Roy / Nicholas Roy Anthony Stentz Matthew / S. Dickerson / /

Position

VP / VP ADVP NP / policy planner / chair / belief space planner / planner / Teller / /

Product

Hierarchical Distributed Correspondence Graph / Pentax K-x Digital Camera / /

ProgrammingLanguage

R / /

ProvinceOrState

Massachusetts / /

PublishedMedium

Machine Learning / Journal of Machine Learning Research / /

Technology

semantic map inference algorithm / Machine Learning / SIMULATION / estimation-theoretic algorithm / /

SocialTag