Robot learning to walk: An architectural problem for intelligent controllers

The authors analyze the design premises of a new control architecture based on the principle of nested hierarchical control which has the feature of learning from the already programmed skills of a teleoperated system. Different architectural principles are explored and compared. It is shown how high level languages can be built from low level knowledge by using cooperation of the already existing skills, new expert knowledge (teaching new heuristics), and self-experience (example based) learning.<<ETX>>

[1]  D. Guinea,et al.  Multi-sensor integration—An automatic feature selection and state identification methodology for tool wear estimation , 1991 .

[2]  Ricardo García Rosa,et al.  Fuzzy logic strategies to control an autonomous mobile robot , 1990 .

[3]  Q. Henry Wu,et al.  A neural network regulator for turbogenerators , 1992, IEEE Trans. Neural Networks.

[4]  Sheng Liu,et al.  Transfer of human skills to neural net robot controllers , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[5]  H. Harry Asada,et al.  Hybrid linguistic/numeric control of deburring robots based on human skills , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[6]  Joseph S. Byrd,et al.  A Six-Legged Telerobot for Nuclear Applications Development , 1990, Int. J. Robotics Res..

[7]  A. Meystel Nested hierarchical control , 1993 .

[8]  A. Meystel,et al.  Optimum design of multiresolutional hierarchical control systems , 1992, Proceedings of the 1992 IEEE International Symposium on Intelligent Control.

[9]  Maja J. Mataric,et al.  Integration of representation into goal-driven behavior-based robots , 1992, IEEE Trans. Robotics Autom..