Natural language instructions for human–robot collaborative manipulation
Abstract
This paper presents a dataset of natural language instructions for object reference in manipulation scenarios. It comprises 1582 individual written instructions, which were collected via online crowdsourcing. This dataset is particularly useful for researchers who work in natural language processing, human–robot interaction, and robotic manipulation. In addition to serving as a rich corpus of domain-specific language, it provides a benchmark of image–instruction pairs to be used in system evaluations and uncovers inherent challenges in tabletop object specification. Example code is provided for easy access via Python.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 11, 2018
- Source ID
- 10.1177/0278364918760992
Entities
People
- Henny Admoni
- Rosario Scalise
- Shen Li
- Siddhartha Srinivasa
- Stephanie Rosenthal
Organizations
- Carnegie Mellon University
- Istituto Superiore di Sanità
- National Science Foundation
- Office of Naval Research
- United States Department of Defense