Learning to Perceive and Act by Trial and Error
Machine Learning
Sensitivity analysis of model output: variance-based methods make the difference
Proceedings of the 29th conference on Winter simulation
Training Personal Robots Using Natural Language Instruction
IEEE Intelligent Systems
Learning to Communicate Through Imitation in Autonomous Robots
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Mobile Robotics: A Practical Introduction
Mobile Robotics: A Practical Introduction
Visual task identification and characterization using polynomial models
Robotics and Autonomous Systems
Robot training using system identification
Robotics and Autonomous Systems
Robot Behaviour: Design, Description, Analysis and Modelling
Robot Behaviour: Design, Description, Analysis and Modelling
Learning forward models for robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Robotics and Autonomous Systems
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Previous research has shown that sensor-motor tasks in mobile robotics applications can be modelled automatically, using NARMAX system identification, where the sensory perception of the robot is mapped to the desired motor commands using non-linear polynomial functions, resulting in a tight coupling between sensing and acting - the robot responds directly to the sensor stimuli without having internal states or memory. However, competences such as for instance sequences of actions, where actions depend on each other, require memory and thus a representation of state. In these cases a simple direct link between sensory perception and the motor commands may not be enough to accomplish the desired tasks. The contribution of this paper to knowledge is to show how fundamental, simple NARMAX models of behaviour can be used in a bootstrapping process to generate complex behaviours that were so far beyond reach. We argue that as the complexity of the task increases, it is important to estimate the current state of the robot and integrate this information into the system identification process. To achieve this we propose a novel method which relates distinctive locations in the environment to the state of the robot, using an unsupervised clustering algorithm. Once we estimate the current state of the robot accurately, we combine the state information with the perception of the robot through a bootstrapping method to generate more complex robot tasks: We obtain a polynomial model which models the complex task as a function of predefined low level sensor-motor controllers and raw sensory data. The proposed method has been used to teach Scitos G5 mobile robots a number of complex tasks, such as advanced obstacle avoidance, or complex route learning.