A Data-driven Approach Towards Human-robot Collaborative Problem Solving in a Shared Space
Abstract
We are developing a system for human-robot communication that enables people to communicate with robots in a natural way and is focused on solving problems in a shared space. Our strategy for developing this system is fundamentally data-driven: we use data from multiple input sources and train key components with various machine learning techniques. We developed a web application that is collecting data on how two humans communicate to accomplish a task, as well as a mobile laboratory that is instrumented to collect data on how two humans communicate to accomplish a task in a physically shared space. The data from these systems will be used to train and fine-tune the second stage of our system, in which the robot will be simulated through software. A physical robot will be used in the final stage of our project. We describe these instruments, a test-suite and performance metrics designed to evaluate and automate the data gathering process as well as evaluate an initial data set.
Cite
@article{arxiv.1710.00274,
title = {A Data-driven Approach Towards Human-robot Collaborative Problem Solving in a Shared Space},
author = {Michael Wollowski and Carlotta Berry and Ryder Winck and Alan Jern and David Voltmer and Alan Chiu and Yosi Shibberu},
journal= {arXiv preprint arXiv:1710.00274},
year = {2017}
}
Comments
2017 AAAI Fall Symposium on Natural Communication for Human-Robot Collaboration