Title
Tell me Dave: Context-sensitive grounding of natural language to manipulation instructions
Abstract
AbstractIt is important for a robot to be able to interpret natural language commands given by a human. In this paper, we consider performing a sequence of mobile manipulation tasks with instructions described in natural language. Given a new environment, even a simple task such as boiling water would be performed quite differently depending on the presence, location and state of the objects. We start by collecting a dataset of task descriptions in free-form natural language and the corresponding grounded task-logs of the tasks performed in an online robot simulator. We then build a library of verb–environment instructions that represents the possible instructions for each verb in that environment, these may or may not be valid for a different environment and task context. We present a model that takes into account the variations in natural language and ambiguities in grounding them to robotic instructions with appropriate environment context and task constraints. Our model also handles incomplete or noisy natural language instructions. It is based on an energy function that encodes such properties in a form isomorphic to a conditional random field. We evaluate our model on tasks given in a robotic simulator and show that it successfully outperforms the state of the art with 61.8% accuracy. We also demonstrate a grounded robotic instruction sequence on a PR2 robot using the Learning from Demonstration approach.
Year
DOI
Venue
2016
10.1177/0278364915602060
Periodicals
Keywords
Field
DocType
Natural language understanding, machine learning, human-robot interaction and crowd-sourcing
Conditional random field,Verb,Instruction sequence,Computer science,Learning from demonstration,Natural language understanding,Natural language,Isomorphism,Artificial intelligence,Robot
Journal
Volume
Issue
ISSN
35
1-3
0278-3649
Citations 
PageRank 
References 
46
1.49
12
Authors
4
Name
Order
Citations
PageRank
Dipendra K. Misra11389.88
Jaeyong Sung239514.51
Kevin Lee345524.23
Ashutosh Saxena44575227.88