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MINERVA: How Small Can a Manipulation Policy Be and Still Solve LIBERO?
Vision-language-action (VLA) models with billions of parameters now dominate the LIBERO manipulation benchmark, but the model capacity actually required by the benchmark remains unclear. We introduce MINERVA (MINimal Efficient Robotic Vision-Action policy), a family of deliberately compact visuomotor policies designed to measure this task-specific capacity floor. A 0.54M-parameter policy achieves 95.1% average success over 2,000 rollouts on the four standard LIBERO suites, only 2.4 points below the reported LeRobot $π_{0.5}$ result despite using 7,700$\times$ fewer parameters. Performance satu
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-03T11:51:10.000Z
First collected: 2026-09-21T04:51:57.792Z. This is not the publication date.