Multi-Armed Recommendation Bandits for Selecting State Machine Policies for Robotic Systems
Venue
Proceedings of International Conference on Robotics and Automation (ICRA 2013)
Publication Year
2013
Authors
Pyry Matikainen, P. Michael Furlong, Rahul Sukthankar, Martial Hebert
BibTeX
Abstract
We investigate the problem of selecting a state-machine from a library to control a
robot. We are particularly interested in this problem when evaluating such state
machines on a particular robotics task is expensive. As a motivating example, we
consider a problem where a simulated vacuuming robot must select a driving state
machine well-suited for a particular (unknown) room layout. By borrowing concepts
from collaborative filtering (recommender systems such as Netflix and Amazon.com),
we present a multi-armed bandit formulation that incorporates recommendation
techniques to efficiently select state machines for individual room layouts. We
show that this formulation outperforms the individual approaches (recommendation,
multi-armed bandits) as well as the baseline of selecting the `average best' state
machine across all rooms.
