Sampling Approach Matters: Active Learning for Robotic Language Acquisition

Author/Creator ORCID




Citation of Original Publication

N. Pillai, E. Raff, F. Ferraro and C. Matuszek, "Sampling Approach Matters: Active Learning for Robotic Language Acquisition," 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 5191-5200, doi: 10.1109/BigData50022.2020.9378415.


This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
© 2020 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora. We present an exploration of active learning approaches applied to three grounded language problems of varying complexity in order to analyze what methods are suitable for improving data efficiency in learning. We present a method for analyzing the complexity of data in this joint problem space, and report on how characteristics of the underlying task, along with design decisions such as feature selection and classification model, drive the results. We observe that representativeness, along with diversity, is crucial in selecting data samples.