Learning from Innate Behaviors: A Quantitative Evaluation of Neural Network Controllers
Machine Learning - Special issue on learning in autonomous robots
Modern Control Systems
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
Learning forward models for robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Stereo- and neural network-based pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
Robotics and Autonomous Systems
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Previously we presented a novel approach to program a robot controller based on system identification and robot training techniques. The proposed method works in two stages: first, the programmer demonstrates the desired behaviour to the robot by driving it manually in the target environment. During this run, the sensory perception and the desired velocity commands of the robot are logged. Having thus obtained training data we model the relationship between sensory readings and the motor commands of the robot using ARMAX/NARMAX models and system identification techniques. These produce linear or non-linear polynomials which can be formally analysed, as well as used in place of ''traditional robot'' control code. In this paper we focus our attention on how the mathematical analysis of NARMAX models can be used to understand the robot's control actions, to formulate hypotheses and to improve the robot's behaviour. One main objective behind this approach is to avoid trial-and-error refinement of robot code. Instead, we seek to obtain a reliable design process, where program design decisions are based on the mathematical analysis of the model describing how the robot interacts with its environment to achieve the desired behaviour. We demonstrate this procedure through the analysis of a particular task in mobile robotics: door traversal.