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Based on the Visual-Language-Action (VLA) model, there is active research on learning robotic manipulation policies that follow verbal instructions and generalize to new situations. In this paper, we present villa-X, a novel framework that integrates latent actions (abstract representations of visual changes between two frames) into VLA pre-training. villa-X improves the integration of latent action learning and VLA pre-training, achieving superior performance in simulation environments such as SIMPLER and LIBERO, as well as in two real-world robotic settings, including grippers and skilled hand manipulation. This demonstrates the importance of the ViLLA paradigm and suggests that villa-X will serve as a foundation for future research.
Takeaways, Limitations
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Takeaways:
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We derive performance improvements for VLA pre-training by improving latent action modeling.
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It has shown excellent robot manipulation policy learning performance in both simulation and real environments.
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We presented the usefulness of the ViLLA paradigm and its potential for future research.
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Limitations:
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Specific Limitations are not explicitly mentioned in the paper. Potential Limitations include generalization degradation, dataset dependency, and computational costs that may occur in real-world applications.
