Recursive Bayesian Human Intent Recognition in Shared-Control Robotics

• Published in: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Vol. 2018; pp. 3905 - 3912
• Main Authors: Jain, Siddarth, Argall, Brenna
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.10.2018
• Subjects: Bayes methods; Hidden Markov models; Mathematical model; Probabilistic logic; Robots; Task analysis; Uncertainty
• ISSN: 2153-0858; 2153-0866
• DOI: 10.1109/IROS.2018.8593766

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.