TY - GEN
T1 - An MRF-Based Intention Recognition Framework for WMRA with Selected Objects as Contextual Clues
AU - Liu, Yan
AU - Yao, Yufeng
AU - Peng, Haoqi
AU - Liu, Yaxin
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - To mitigate the physical burden of disabled people, we propose an approach that a robot could perceive the implied action intentions of disabled people by their selected objects. This article presents a framework for recognizing and learning human intentions based on selected household objects and interaction history. First, the intention network is modeled based on Markov random field (MRF) to connect the selected objects and daily activities. Second, the q-learning algorithm is added to provide the intention network with the function of adapting to the user’s intention preference. Then, we build the wheelchair-mounted robotic arms (WMRA) with a green laser pointer as human-robot interaction (HRI). Finally, we demonstrate the feasibility of the intention recognition framework by evaluating a scene comprised of objects from 11 categories, along with 7 possible actions, 36 single-object intentions, and 24 multiple-object intentions. We achieve approximately 70% reduction in fewer sessions than the Recursive Bayesian Incremental Learning and achieve approximately 87.5% and 86.2% reduction in interactions overall on recognizing multiple-object intention, respectively.
AB - To mitigate the physical burden of disabled people, we propose an approach that a robot could perceive the implied action intentions of disabled people by their selected objects. This article presents a framework for recognizing and learning human intentions based on selected household objects and interaction history. First, the intention network is modeled based on Markov random field (MRF) to connect the selected objects and daily activities. Second, the q-learning algorithm is added to provide the intention network with the function of adapting to the user’s intention preference. Then, we build the wheelchair-mounted robotic arms (WMRA) with a green laser pointer as human-robot interaction (HRI). Finally, we demonstrate the feasibility of the intention recognition framework by evaluating a scene comprised of objects from 11 categories, along with 7 possible actions, 36 single-object intentions, and 24 multiple-object intentions. We achieve approximately 70% reduction in fewer sessions than the Recursive Bayesian Incremental Learning and achieve approximately 87.5% and 86.2% reduction in interactions overall on recognizing multiple-object intention, respectively.
KW - Human-robot interaction
KW - Intention recognition
KW - Markov random field
KW - Wheel mounted robotic arms
UR - https://www.scopus.com/pages/publications/85118132979
U2 - 10.1007/978-3-030-89134-3_32
DO - 10.1007/978-3-030-89134-3_32
M3 - 会议稿件
AN - SCOPUS:85118132979
SN - 9783030891336
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 345
EP - 356
BT - Intelligent Robotics and Applications - 14th International Conference, ICIRA 2021, Proceedings
A2 - Liu, Xin-Jun
A2 - Nie, Zhenguo
A2 - Yu, Jingjun
A2 - Xie, Fugui
A2 - Song, Rui
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th International Conference on Intelligent Robotics and Applications, ICIRA 2021
Y2 - 22 October 2021 through 25 October 2021
ER -