Library of Hands: Fix the Hands, Not the World Model
Overview
This research presents Library of Hands (LoH), a training-free approach that corrects command-to-action mappings without retraining the world model. By identifying actions that best explain changes between camera images, LoH stores identified hands to recognize returning setups rather than re-learn them. In closed-loop tests on three simulated tasks, LoH more than doubles success rates from 28.6% to 62.3%, with minimal performance loss when conditions remain unchanged. The method works unchanged with two other world models and on two of three models pretrained on real robot data, with gains increasing as reading quality improves.
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- arXiv Robotics — research abstractsSignalLibrary of Hands: Fix the Hands, Not the World Model
This research presents Library of Hands (LoH), a training-free approach that corrects command-to-action mappings without retraining the world model. By identifying actions that best explain changes between camera images, LoH stores identified hands to recognize returning setups rather than re-learn them. In closed-loop tests on three simulated tasks, LoH more than doubles success rates from 28.6% to 62.3%, with minimal performance loss when conditions remain unchanged. The method works unchanged with two other world models and on two of three models pretrained on real robot data, with gains increasing as reading quality improves.
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