Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3905-3912 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781538680940 |
| DOIs | |
| State | Published - Dec 27 2018 |
| Event | 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 - Madrid, Spain Duration: Oct 1 2018 → Oct 5 2018 |
Publication series
| Name | IEEE International Conference on Intelligent Robots and Systems |
|---|---|
| ISSN (Print) | 2153-0858 |
| ISSN (Electronic) | 2153-0866 |
Conference
| Conference | 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 |
|---|---|
| Country | Spain |
| City | Madrid |
| Period | 10/1/18 → 10/5/18 |
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ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Computer Vision and Pattern Recognition
- Computer Science Applications
Cite this
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Recursive Bayesian Human Intent Recognition in Shared-Control Robotics. / Jain, Siddarth; Argall, Brenna Dee.
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3905-3912 8593766 (IEEE International Conference on Intelligent Robots and Systems).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Recursive Bayesian Human Intent Recognition in Shared-Control Robotics
AU - Jain, Siddarth
AU - Argall, Brenna Dee
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
UR - http://www.scopus.com/inward/citedby.url?scp=85063010190&partnerID=8YFLogxK
U2 - 10.1109/IROS.2018.8593766
DO - 10.1109/IROS.2018.8593766
M3 - Conference contribution
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.
ER -