TY - GEN
T1 - Designing POMDP models of socially situated tasks
AU - Broz, F.
AU - Nourbakhsh, I.
AU - Simmons, Reid
N1 - “This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."
“Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”
PY - 2011/1/1
Y1 - 2011/1/1
N2 - In this paper, a modelling approach is described that represents human-robot social interactions as partially observable Markov decision processes (POMDPs). In these POMDPs, the intention of the human is represented as an unobservable part of the state space, and the robot's own intentions are expressed through the rewards. The state transition structure for the models is created using action rules that capture the effects of the robot's actions, relate the human's behavior to their intentions, and describe the changing state of the environment. State transitions are modified using data from humans interacting with other humans. The policies obtained by solving these models are used to control a robot in a socially situated task with a human partner. These interactions are compared to those of human pairs performing the same task, demonstrating that this approach produces policies that exhibit natural and socially appropriate behavior.
AB - In this paper, a modelling approach is described that represents human-robot social interactions as partially observable Markov decision processes (POMDPs). In these POMDPs, the intention of the human is represented as an unobservable part of the state space, and the robot's own intentions are expressed through the rewards. The state transition structure for the models is created using action rules that capture the effects of the robot's actions, relate the human's behavior to their intentions, and describe the changing state of the environment. State transitions are modified using data from humans interacting with other humans. The policies obtained by solving these models are used to control a robot in a socially situated task with a human partner. These interactions are compared to those of human pairs performing the same task, demonstrating that this approach produces policies that exhibit natural and socially appropriate behavior.
UR - http://www.scopus.com/inward/record.url?scp=80053021834&partnerID=8YFLogxK
U2 - 10.1109/ROMAN.2011.6005264
DO - 10.1109/ROMAN.2011.6005264
M3 - Conference contribution
AN - SCOPUS:80053021834
SN - 978-145771571-6
VL - 6005264
SP - 39
EP - 46
BT - Proceedings - IEEE International Workshop on Robot and Human Interactive Communication
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - RO-MAN 2011
Y2 - 31 July 2011 through 3 August 2011
ER -