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This paper describes a robot manipulator system currently under development which learns from observation. The system improves its problem-solving capabilities through the acquisition of task-related concepts. The system observes manipulator command sequences that solve problems currently beyond its own panning abilities. General problem-solving schemata are automatically constructed via a knowledge-based analysis of how the observed command sequence achieved the goal. This learning technique is based on explanatory schema acquisition. It is a knowledge-based approach, requiring sufficient background knowledge to understand the observed sequence. The acquired schemata serve two purposes: they allow the system to solve problems that were previously unsolvable, and they aid in the understanding of later observations.