Increasingly more unstructured environments of today’s industry challenge the robots to have the capability to dynamically adapt to variations in the part sizes and positions. Traditional programming methods fall short of answering such needs. Programming by demonstration is an approach that allows the robots to learn tasks from human demonstrations. Improvements in the generalization of individual tasks that compose the complex assembly operations are an indispensable need for a more extensive adoption of PbD in the industry. This thesis aims to improve the generalization of the peg-in-hole task against variations in the hole positions. It uses the change in the hole position a metric for the novelty of the task and tests the success rate at increasing distances. The relationship between the novelty of the task and its success is examined for two different learning strategies. In the first strategy, only the positional characteristics of the task are learned, whereas both positional and force characteristics are learned in the latter. It is found that the success rate of the task decreases in both cases as the distance increases. However, the hybrid position/force learning strategy outperforms the purely positional one at all distances. As a result, this strategy is experimentally shown to be a valid approach to improve the generalization of the peg-in-hole task for changing hole positions. Incorporation of this strategy with existing frameworks and orientation generalization methods is suggested as future work.