Learning the behavior model of a robot

• Published in: Autonomous robots 2011-02, Vol.30 (2), p.157-177
• Main Authors: Infantes, Guillaume, Ghallab, Malik, Ingrand, Félix
• Format: Article
• Language: English
• Subjects: Actuators; Algorithms; Artificial Intelligence; Autonomous; Autonomous navigation; Bayesian analysis; Computer Imaging; Control; Engineering; Environment models; Learning; Mechatronics; Pattern Recognition and Graphics; Probabilistic models; Robotics; Robotics and Automation; Robots; Task complexity; Vision
• ISSN: 0929-5593; 1573-7527
• DOI: 10.1007/s10514-010-9212-1

Complex artifacts are designed today from well specified and well modeled components. But most often, the models of these components cannot be composed into a global functional model of the artifact. A significant observation, modeling and identification effort is required to get such a global model, which is needed in order to better understand, control and improve the designed artifact. Robotics provides a good illustration of this need. Autonomous robots are able to achieve more and more complex tasks, relying on more advanced sensor-motor functions. To better understand their behavior and improve their performance, it becomes necessary but more difficult to characterize and to model, at the global level, how robots behave in a given environment. Low-level models of sensors, actuators and controllers cannot be easily combined into a behavior model. Sometimes high level models operators used for planning are also available, but generally they are too coarse to represent the actual robot behavior. We propose here a general framework for learning from observation data the behavior model of a robot when performing a given task. The behavior is modeled as a Dynamic Bayesian Network , a convenient stochastic structured representations. We show how such a probabilistic model can be learned and how it can be used to improve, on line, the robot behavior with respect to a specific environment and user preferences. Framework and algorithms are detailed; they are substantiated by experimental results for autonomous navigation tasks.