Experimentation-Driven Operator Learning

Kang Soo Tae

Expert-provided operator descriptions are expensive, incomplete, and incorrect. Given the assumptions of noise-free information and an completely-observable state, OBSERVER can autonomously learn and refines new operators through observation and practice (Wang 1995). WISER, our learning system, relaxes these assumptions and learns operator preconditions through experimentation utilizing imperfect expert-provided knowledge. Our decision-theoretic formula calculates a probably best state S' for experimentation based on the imperfect knowledge. When a robotic action is executed successfully for the first time in a state S, the corresponding operator’s initial preconditions are learned as parameterized S. We empirically show the number of training examples required to learn the initial preconditions as a function of the amount of injected error. The learned preconditions contain all the necessary positive literals, but no negative literals.

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