Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations
Overview
This research introduces DR-LfD, a framework that integrates visuomotor policies into a task and motion planning system to automate dexterous bimanual manipulation. By decomposing human demonstrations into atomic skills based on contact relationships, the method reduces data requirements for skill composition. Benchmarks show that DR-LfD outperforms diffusion-based imitation policies using significantly fewer demonstrations in unseen setups.
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Reported quantity · demonstrations: 100 other · Basis not reported · Differing source assertions
“stration burden scales with the number of distinct skill types, with limited data for each skill. Real-world and simulation benchmarks demonstrate data-efficient skill composition under unseen setups and physical constraints. On two DexMimicGen tasks, DR-LfD with 100 demonstrations outperforms diffusion-based imitation policies with 1000.”
Exact source · revision 1Source owner not reported
Reported quantity · demonstrations: 1000 other · Basis not reported · Differing source assertions
“stration burden scales with the number of distinct skill types, with limited data for each skill. Real-world and simulation benchmarks demonstrate data-efficient skill composition under unseen setups and physical constraints. On two DexMimicGen tasks, DR-LfD with 100 demonstrations outperforms diffusion-based imitation policies with 1000.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalDecompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations
This research introduces DR-LfD, a framework that integrates visuomotor policies into a task and motion planning system to automate dexterous bimanual manipulation. By decomposing human demonstrations into atomic skills based on contact relationships, the method reduces data requirements for skill composition. Benchmarks show that DR-LfD outperforms diffusion-based imitation policies using significantly fewer demonstrations in unseen setups.
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