Macro-Operator Generation and Predicate Selection for TAMP Operator Learning
Macro-Operator Generation and Predicate Selection for TAMP Operator Learning
This paper introduces a system that automatically generates macro-operators by discovering causally linked action pairs from demonstration data, while simultaneously pruning irrelevant predicates to reduce the symbolic state. The approach addresses bottlenecks in Task and Motion Planning (TAMP) where existing methods learn actions in isolation or evaluate unnecessary predicates. By compressing recurring multi-step manipulation sequences into single planning steps and shrinking the search space, the method achieves up to a ~4.6x speedup over the baseline 'Learning Operators for TAMP' across four domains. Crucially, it enables solving long sequential tasks that the baseline fails to solve, demonstrating that macro-operator discovery can determine practical solvability in long-horizon scenarios. The code is available at https://github.com/canemirbora4/macro-tamp.
The authors present a method for automatically generating macro-operators and pruning predicates to improve Task and Motion Planning (TAMP) efficiency, reporting up to a ~4.6x planning speedup across four domains and solving long sequential tasks that a baseline cannot handle.
Source: arXiv Robotics — research abstracts · Read original article ↗
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and the benefit they bring grows with the length of the task. Across four TAMP domains, our method reaches up to a ~4.6x planning speedup compared to the baseline method, namely Learning Operators for TAMP. More importantly, it solves a long sequential task that the baseline cannot solve. Macro-operator discovery thus not only accelerates planning but, in our long-horizon domain, determines solvability in practice. O
Open source S5
ur code is available at https://github.com/canemirbora4/macro-tamp
Open source S6
Source：arXiv Robotics — research abstracts · arxiv.org