TY - GEN
T1 - Recursive Bayesian Human Intent Recognition in Shared-Control Robotics
AU - Jain, Siddarth
AU - Argall, Brenna Dee
N1 - Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/12/27
Y1 - 2018/12/27
N2 - Effective human-robot collaboration in shared control requires reasoning about the intentions of the human user. In this work, we present a mathematical formulation for human intent recognition during assistive teleoperation under shared autonomy. Our recursive Bayesian filtering approach models and fuses multiple non-verbal observations to probabilistically reason about the intended goal of the user. In addition to contextual observations, we model and incorporate the human agent's behavior as goal-directed actions with adjustable rationality to inform the underlying intent. We examine human inference on robot motion and furthermore validate our approach with a human subjects study that evaluates autonomy intent inference performance under a variety of goal scenarios and tasks, by novice subjects. Results show that our approach outperforms existing solutions and demonstrates that the probabilistic fusion of multiple observations improves intent inference and performance for shared-control operation.
AB - Effective human-robot collaboration in shared control requires reasoning about the intentions of the human user. In this work, we present a mathematical formulation for human intent recognition during assistive teleoperation under shared autonomy. Our recursive Bayesian filtering approach models and fuses multiple non-verbal observations to probabilistically reason about the intended goal of the user. In addition to contextual observations, we model and incorporate the human agent's behavior as goal-directed actions with adjustable rationality to inform the underlying intent. We examine human inference on robot motion and furthermore validate our approach with a human subjects study that evaluates autonomy intent inference performance under a variety of goal scenarios and tasks, by novice subjects. Results show that our approach outperforms existing solutions and demonstrates that the probabilistic fusion of multiple observations improves intent inference and performance for shared-control operation.
UR - http://www.scopus.com/inward/record.url?scp=85063010190&partnerID=8YFLogxK
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U2 - 10.1109/IROS.2018.8593766
DO - 10.1109/IROS.2018.8593766
M3 - Conference contribution
C2 - 32300492
AN - SCOPUS:85063010190
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 3905
EP - 3912
BT - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
Y2 - 1 October 2018 through 5 October 2018
ER -