Interactive Task Learning (ITL) focuses on learning the definition of tasks through online natural language instruction in real time. Learning the correct grounded meaning of the instructions is difficult due to ambiguous words, lack of common ground, and the presence of distractors in the environment and the agent's knowledge. We present a learning strategy embodied in an ITL agent that interactively learns in one shot the meaning of task concepts for 40 games and puzzles in ambiguous scenarios. Our approach learns hierarchical symbolic representations of task knowledge rather than learning a mapping directly from perceptual representations. These representations enable the agent to transfer and compose knowledge, analyze and debug multiple interpretations, and communicate efficiently with the teacher to resolve ambiguity. We evaluate the efficiency of the learning by examining the number of words required to teach tasks across cases of no transfer, positive transfer, and interference from prior tasks. Our results show that the agent can correctly generalize, disambiguate, and transfer concepts within variations in language descriptions and world representations of the same task, and across variations in different tasks.
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