Exploring Variation of Natural Human Commands to a Robot in a Collaborative Navigation Task
Matthew Marge, Claire Bonial, Ashley Foots, Cory Hayes, Cassidy Henry, Kimberly Pollard, Ron Artstein, Clare Voss, David Traum
Abstract
Robot-directed communication is variable, and may change based on human perception of robot capabilities. To collect training data for a dialogue system and to investigate possible communication changes over time, we developed a Wizard-of-Oz study that (a) simulates a robot’s limited understanding, and (b) collects dialogues where human participants build a progressively better mental model of the robot’s understanding. With ten participants, we collected ten hours of human-robot dialogue. We analyzed the structure of instructions that participants gave to a remote robot before it responded. Our findings show a general initial preference for including metric information (e.g., move forward 3 feet) over landmarks (e.g., move to the desk) in motion commands, but this decreased over time, suggesting changes in perception.- Anthology ID:
- W17-2808
- Volume:
- Proceedings of the First Workshop on Language Grounding for Robotics
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Mohit Bansal, Cynthia Matuszek, Jacob Andreas, Yoav Artzi, Yonatan Bisk
- Venue:
- RoboNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 58–66
- Language:
- URL:
- https://aclanthology.org/W17-2808
- DOI:
- 10.18653/v1/W17-2808
- Cite (ACL):
- Matthew Marge, Claire Bonial, Ashley Foots, Cory Hayes, Cassidy Henry, Kimberly Pollard, Ron Artstein, Clare Voss, and David Traum. 2017. Exploring Variation of Natural Human Commands to a Robot in a Collaborative Navigation Task. In Proceedings of the First Workshop on Language Grounding for Robotics, pages 58–66, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Exploring Variation of Natural Human Commands to a Robot in a Collaborative Navigation Task (Marge et al., RoboNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W17-2808.pdf