Algorithms & data structures
Model-based control of a robot manipulator
Model-based control of a robot manipulator
Compliant robot motion: I. A formalism for specifying compliant motion tasks
International Journal of Robotics Research
Compliant robot motion II. A control approach based on external control loops
International Journal of Robotics Research
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Autonomous robot calibration for hand-eye coordination
International Journal of Robotics Research
An introduction to fuzzy control
An introduction to fuzzy control
A computational description of the organization of human reaching and prehension
A computational description of the organization of human reaching and prehension
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
The neural network model RuleNet and its application to mobile robot navigation
Fuzzy Sets and Systems - Special issue on methods for data analysis in classificatin and control
Learning the peg-into-hole assembly operation with a connectionist reinforcement technique
Computers in Industry - Special issue on learning in intelligent manufacturing systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Robot Learning
Learning in Multi-Robot Systems
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Evolution of homing navigation in a real mobile robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper explores a stepwise learning approach based on asystem’s decomposition into functional subsystems. Two case studiesare examined: a visually guided robot that learns to track a maneuveringobject, and a robot that learns to use the information from a force sensorin order to put a peg into a hole. These two applications show the featuresand advantages of the proposed approach: i) the subsystems naturally ariseas functional components of the hardware and software; ii) these subsystemsare building blocks of the robot behavior and can be combined in severalways for performing various tasks; iii) this decomposition makes it easierto check the performances and detect the cause of a malfunction; iv) onlythose subsystems for which a satisfactory solution is not available need tobe learned; v) the strategy proposed for coordinating the optimization ofall subsystems ensures an improvement at the task-level; vi) the overallsystem’s behavior is significantly improved by the stepwise learningapproach.