Primitive Manipulation Learning with Connectionism

Infants' manipulative exploratory behavior within the environment is a vehicle of cognitive stimulation[McCall 1974]. During this time, infants practice and perfect sensorimotor patterns that become behavioral modules which will be seriated and imbedded in more complex actions. This paper explores the development of such primitive learning systems using an embodied lightweight hand which will be used for a humanoid being developed at the MIT Artificial Intelligence Laboratory[Brooks and Stein 1993]. Primitive grasping procedures are learned from sensory inputs using a connectionist reinforcement algorithm while two submodules preprocess sensory data to recognize the hardness of objects and detect shear using competitive learning and back-propagation algorithm strategies, respectively. This system is not only consistent and quick during the initial learning stage, but also adaptable to new situations after training is completed.