Asking for Help Using Inverse Semantics
Name
tellex14.pdf
Size
2.33 MB
Format
Adobe PDF
Checksum (MD5)
dc09d78273c4e179b5cd3ecb17c8ea10
Author(s)
Knepper, Ross A.
Li, Adrian Ling Hin
Rus, Daniela L
Roy, Nicholas
Tellex, Stefanie A.
Date Issued
July 2014
Journal
Robotics: Science and Systems X
Publisher
Robotics: Science and Systems Foundation
Citation
Tellex, Stefanie, Ross Knepper, Adrian Li, Daniela Rus, and Nicholas Roy. “Asking for Help Using Inverse Semantics.” Robotics: Science and Systems X (July 12, 2014).
Version
Author's final manuscript
Abstract
Robots inevitably fail, often without the ability to recover autonomously. We demonstrate an approach for enabling a robot to recover from failures by communicating its need for specific help to a human partner using natural language. Our approach automatically detects failures, then generates targeted spoken-language requests for help such as "`Please give me the white table leg that is on the black table.'' Once the human partner has repaired the failure condition, the system resumes full autonomy. We present a novel inverse semantics algorithm for generating effective help requests. In contrast to forward semantic models that interpret natural language in terms of robot actions and perception, our inverse semantics algorithm generates requests by emulating the human's ability to interpret a request using the Generalized Grounding Graph (G³) framework. To assess the effectiveness of our approach, we present a corpus-based online evaluation, as well as an end-to-end user study, demonstrating that our approach increases the effectiveness of human interventions compared to static requests for help.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.15607/RSS.2014.X.024
