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Discovering natural kinds of robot sensory experiences in unstructured environments
Author(s) -
Grollman Daniel H.,
Jenkins Odest Chadwicke,
Wood Frank
Publication year - 2006
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.20160
Subject(s) - isomap , artificial intelligence , computer science , nonlinear dimensionality reduction , cluster analysis , robot , mixture model , dimensionality reduction , raw data , pattern recognition (psychology) , machine learning , programming language
We address the symbol grounding problem for robot perception through a data‐driven approach to deriving categories from robot sensor data. Unlike model‐based approaches, where human intuitive correspondences are sought between sensor readings and features of an environment (corners, doors, etc.), our method learns intrinsic categories (or natural kinds) from the raw data itself. We approximate a manifold underlying sensor data using Isomap nonlinear dimension reduction and apply Bayesian clustering (Gaussian mixture models) with model identification techniques to discover categories (or kinds). We demonstrate our method through the learning of sensory kinds from trials in various indoor and outdoor environments with different sensor modalities. Learned kinds are then used to classify new sensor data (out‐of‐sample readings). We present results indicating greater consistency in classifying sensor data employing mixture models in nonlinear low‐dimensional embeddings. © 2007 Wiley Periodicals, Inc.