We present a modified version of Extended Classifier System (XCS) on a humanoid NAO robot. The robot is capable of learning a complete, accurate, and maximally general map of an environment through evolutionary search and reinforcement learning. The standard alternation between explore and exploit trials is revised so that the robot relearns only when necessary. This modification makes the learning more effective and provides the XCS with external memory to evaluate the environmental change. Furthermore, it overcomes the drawbacks of learning rate settings in traditional XCS. A simple object seeking task is presented which demonstrates the desirable adaptivity of LCS for a sequential task on a real robot in dynamic environments.
|Title of host publication||2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012|
|Number of pages||6|
|Publication status||Published - 1 Dec 2012|
|Event||2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012 - Guangzhou, China|
Duration: 5 Dec 2012 → 7 Dec 2012
|Conference||2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012|
|Period||5/12/12 → 7/12/12|