Macro-Operator Generation and Predicate Selection for TAMP Operator Learning

The paper introduces a system that automatically generates macro-operators—composite actions that compress recurring sequences of individual actions—by discovering causally linked action pairs in training data. It also prunes unused predicates from the symbolic state, reducing the number of predicates evaluated at each search node. These combined optimizations shorten the effective planning horizon and yield up to a 4.6× speedup, enabling the solution of long sequential tasks that baseline methods cannot solve.

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• robotics