Zero-Shot VLA Adaptation with Retrieved Guidance
Overview
This paper presents RAGu, a method for adapting Vision-Language-Action (VLA) models to new domains by retrieving and guiding behavior from domain-mismatched demonstration datasets. The approach incorporates geometric calibration to align source demonstrations with the target setup and employs a coarse-to-fine retrieval strategy to select compatible trajectories. Instead of retraining the base model or expanding its context window, RAGu guides the VLA's flow-matching inference procedure. Evaluations across real-world, simulation, sim-to-real transfer, and cross-embodiment humanoid manipulation show that RAGu improves real-world VLA success rates from 20% to 80% and outperforms related baselines in simulation.
Generated from attributed reports · Updated 11 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · Reported success rate: 20 percent · Basis not reported · Differing source assertions
“eal-world, simulation, sim-to-real transfer, and cross-embodiment humanoid manipulation. We find it improves real-world VLA success rates from 20% to 80% and outperforms the closest-related baselines in sim. Project website: raguvla.github.io”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 80 percent · Basis not reported · Differing source assertions
“eal-world, simulation, sim-to-real transfer, and cross-embodiment humanoid manipulation. We find it improves real-world VLA success rates from 20% to 80% and outperforms the closest-related baselines in sim. Project website: raguvla.github.io”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalZero-Shot VLA Adaptation with Retrieved Guidance
This paper presents RAGu, a method for adapting Vision-Language-Action (VLA) models to new domains by retrieving and guiding behavior from domain-mismatched demonstration datasets. The approach incorporates geometric calibration to align source demonstrations with the target setup and employs a coarse-to-fine retrieval strategy to select compatible trajectories. Instead of retraining the base model or expanding its context window, RAGu guides the VLA's flow-matching inference procedure. Evaluations across real-world, simulation, sim-to-real transfer, and cross-embodiment humanoid manipulation show that RAGu improves real-world VLA success rates from 20% to 80% and outperforms related baselines in simulation.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).