Using associative content-addressable memories to control robots

The use of an associative content-addressable memory to model a robot and the world the robot interacts with is discussed. The model can be learned by storing experiences in the memory. To make predictions, the memory is searched for relevant experience. An initial implementation of such a memory-based modeling scheme has been made on a parallel computer, the Connection Machine. The implementation machine was used to model and control a simulated planar two-joint arm and a simulated running machine. The issues and problems that arose in the preliminary work are described. It is found that the use of parallel search in the implementation of an associative content-addressable memory allows quick searching of stored experiences, and reasonable retrieval is obtained using a simple distance metric and a simple generalized scheme. The memory is able to generalize after storing only a small number of relevant experiences. The use of search by parallel processors also avoids many of the problems of previous memory-based or tubular approaches to robot modeling (such as search speed and memory requirements)